diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml
index 9bee1c1f4..64300fcbb 100644
--- a/.pre-commit-config.yaml
+++ b/.pre-commit-config.yaml
@@ -2,21 +2,32 @@ default_stages: [ "commit", "commit-msg", "push" ]
default_language_version:
python: python3
-
repos:
- - repo: https://github.com/timothycrosley/isort
- rev: 5.13.2
+ - repo: https://github.com/astral-sh/ruff-pre-commit
+ # Ruff version.
+ rev: v0.4.8
hooks:
- - id: isort
+ # Run the linter.
+ - id: ruff
+ types_or: [ python ]
+ args: [ --fix ]
+ # Run the formatter.
+ - id: ruff-format
+ types_or: [ python, pyi, jupyter ]
- - repo: https://github.com/psf/black
- rev: 24.2.0
- hooks:
- - id: black
- name: "Code formatter"
+ # - repo: https://github.com/timothycrosley/isort
+ # rev: 5.13.2
+ # hooks:
+ # - id: isort
+
+ # - repo: https://github.com/psf/black
+ # rev: 24.2.0
+ # hooks:
+ # - id: black
+ # name: "Code formatter"
- repo: https://github.com/pre-commit/pre-commit-hooks
- rev: v4.5.0
+ rev: v4.6.0
hooks:
- id: end-of-file-fixer
name: "End of file fixer"
@@ -32,20 +43,20 @@ repos:
- id: trailing-whitespace
name: "Trailing whitespace fixer"
- - repo: https://github.com/PyCQA/flake8
- rev: 7.0.0
- hooks:
- - id: flake8
- name: "Linter"
- additional_dependencies:
- - pep8-naming
- - flake8-builtins
- - flake8-comprehensions
- - flake8-bugbear
- - flake8-pytest-style
- - flake8-cognitive-complexity
- - flake8-pyproject
- - importlib-metadata<5.0
+ # - repo: https://github.com/PyCQA/flake8
+ # rev: 7.0.0
+ # hooks:
+ # - id: flake8
+ # name: "Linter"
+ # additional_dependencies:
+ # - pep8-naming
+ # - flake8-builtins
+ # - flake8-comprehensions
+ # - flake8-bugbear
+ # - flake8-pytest-style
+ # - flake8-cognitive-complexity
+ # - flake8-pyproject
+ # - importlib-metadata<5.0
- repo: local
hooks:
@@ -57,7 +68,7 @@ repos:
pass_filenames: false
- repo: https://github.com/alessandrojcm/commitlint-pre-commit-hook
- rev: v9.11.0
+ rev: v9.16.0
hooks:
- id: commitlint
name: "Commit linter"
@@ -65,7 +76,7 @@ repos:
additional_dependencies: [ '@commitlint/config-conventional' ]
- repo: https://github.com/Lucas-C/pre-commit-hooks
- rev: v1.3.0
+ rev: v1.5.5
hooks:
- id: insert-license
name: "License inserter"
diff --git a/examples/Quickstart.ipynb b/examples/Quickstart.ipynb
index d9d966955..734415c69 100644
--- a/examples/Quickstart.ipynb
+++ b/examples/Quickstart.ipynb
@@ -1,1208 +1,1226 @@
{
- "cells": [
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "8uCEQLS3zZUn"
- },
- "source": [
- "# Mava Quickstart Notebook\n",
- "\n"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "0Ajb_J4W1u4V"
- },
- "source": [
- ""
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "a99IjmO51uP2"
- },
- "source": [
- "### This notebook offers a simple introduction to [Mava](https://github.com/instadeepai/Mava) by showing how to build and train a multi-agent PPO (MAPPO) system on the RobotWarehouse environment from [Jumanji](https://github.com/instadeepai/jumanji). Mava follows the design philosophy of [CleanRL](https://github.com/vwxyzjn/cleanrl) allowing for easy code readability and reuse, and is built on top of code from [PureJaxRL](https://github.com/luchris429/purejaxrl), extending it to provide end-to-end JAX-based multi-agent algorithms.\n",
- "\n",
- "![\"Open](\"https://colab.research.google.com/assets/colab-badge.svg\")
\n"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "LYmyi-lU3a-b"
- },
- "source": [
- "# Requirements\n",
- "\n",
- "We start by installing and importing the necessary packages."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "cellView": "form",
- "id": "5l-eEkH-2f0D"
- },
- "outputs": [],
- "source": [
- "%%capture\n",
- "# @title Install Mava\n",
- "! pip install git+https://github.com/instadeepai/mava.git@develop\n",
- "! pip install \"jax[cuda12_pip]\" -f https://storage.googleapis.com/jax-releases/jax_cuda_releases.html"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "IMBnurbl-9Ez"
- },
- "source": [
- "Restarting the runtime is necessary after reinstalling JAX in Colab to ensure that the changes take effect and that the runtime environment is properly configured for the updated JAX version."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "cellView": "form",
- "id": "2pMV4rGjTQAw"
- },
- "outputs": [],
- "source": [
- "# @title Restart Google Colab runtime\n",
- "import os\n",
- "os.kill(os.getpid(), 9)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 1,
- "metadata": {
- "cellView": "form",
- "id": "FjXA8JyI1_YW"
- },
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "/home/wiemkhlifi/miniconda3/envs/mava/lib/python3.9/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
- " from .autonotebook import tqdm as notebook_tqdm\n"
- ]
- },
- {
- "data": {
- "text/html": [
- ""
- ],
- "text/plain": [
- "[(0.00392156862745098, 0.45098039215686275, 0.6980392156862745),\n",
- " (0.8705882352941177, 0.5607843137254902, 0.0196078431372549),\n",
- " (0.00784313725490196, 0.6196078431372549, 0.45098039215686275),\n",
- " (0.8352941176470589, 0.3686274509803922, 0.0),\n",
- " (0.8, 0.47058823529411764, 0.7372549019607844),\n",
- " (0.792156862745098, 0.5686274509803921, 0.3803921568627451),\n",
- " (0.984313725490196, 0.6862745098039216, 0.8941176470588236),\n",
- " (0.5803921568627451, 0.5803921568627451, 0.5803921568627451),\n",
- " (0.9254901960784314, 0.8823529411764706, 0.2),\n",
- " (0.33725490196078434, 0.7058823529411765, 0.9137254901960784)]"
- ]
- },
- "execution_count": 1,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "#@title Import required packages.\n",
- "\n",
- "import time\n",
- "from typing import Any, Sequence, Tuple\n",
- "\n",
- "import chex\n",
- "import flax\n",
- "import flax.linen as nn\n",
- "import jax\n",
- "import jax.numpy as jnp\n",
- "\n",
- "# Env requirements\n",
- "import jumanji\n",
- "\n",
- "# Plot requirements\n",
- "import matplotlib.pyplot as plt\n",
- "import numpy as np\n",
- "import optax\n",
- "\n",
- "import tensorflow_probability.substrates.jax.distributions as tfd\n",
- "from colorama import Fore, Style\n",
- "from flax.core.frozen_dict import FrozenDict\n",
- "from flax.linen.initializers import orthogonal\n",
- "from IPython.display import clear_output\n",
- "from omegaconf import DictConfig, OmegaConf\n",
- "from optax._src.base import OptState\n",
- "\n",
- "# Mava Helpful functions and types\n",
- "from mava.distributions import IdentityTransformation\n",
- "from mava.evaluator import make_eval_fns\n",
- "from mava.systems.ppo.types import LearnerState, OptStates, Params, PPOTransition\n",
- "from mava.types import (\n",
- " ActorApply,\n",
- " CriticApply,\n",
- " ExperimentOutput,\n",
- " LearnerFn,\n",
- " Observation,\n",
- " ObservationGlobalState,\n",
- ")\n",
- "from mava.utils.jax_utils import merge_leading_dims, unreplicate_batch_dim, unreplicate_n_dims\n",
- "from mava.utils.training import make_learning_rate\n",
- "from mava.wrappers import (\n",
- " AgentIDWrapper,\n",
- " AutoResetWrapper,\n",
- " RecordEpisodeMetrics,\n",
- " RwareWrapper,\n",
- ")\n",
- "\n",
- "%matplotlib inline\n",
- "import seaborn as sns\n",
- "sns.set()\n",
- "sns.set_style(\"white\")\n",
- "sns.color_palette(\"colorblind\")"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "9omksZSH6htZ"
- },
- "source": [
- "# Trainer\n",
- "This section encompasses the foundational methods required to set up the training process for MAPPO.\n"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "JaIw_5YaUSAB"
- },
- "source": [
- "### Network\n",
- "\n",
- "Initially, we start by constructing the Actor and Critic networks using components from the Flax library.\n",
- "\n",
- "* The `Actor()` network takes an observation as input and produces logits representing the probabilities of different actions. The shapes within the network are determined dynamically based on the number of agents, the observation, and the batch size.\n",
- "* The `Critic()` network takes the global state as input and produces the estimated value of the state. Similar to the Actor network, the shapes within the network are handled implicitly by Flax."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 2,
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/"
- },
- "id": "Sss6opmC6lmp",
- "outputId": "7eb3833d-d44b-4218-c9f4-aae8aa2e447c"
- },
- "outputs": [],
- "source": [
- "class Actor(nn.Module):\n",
- " \"\"\"Actor Network.\"\"\"\n",
- "\n",
- " action_dim: Sequence[int]\n",
- "\n",
- " @nn.compact\n",
- " def __call__(self, observation: Observation) -> tfd.TransformedDistribution:\n",
- " \"\"\"Forward pass.\"\"\"\n",
- " x = observation.agents_view\n",
- "\n",
- " actor_output = nn.Dense(128, kernel_init=orthogonal(np.sqrt(2)))(x)\n",
- " actor_output = nn.relu(actor_output)\n",
- " actor_output = nn.Dense(128, kernel_init=orthogonal(np.sqrt(2)))(\n",
- " actor_output\n",
- " )\n",
- " actor_output = nn.relu(actor_output)\n",
- " actor_output = nn.Dense(\n",
- " self.action_dim, kernel_init=orthogonal(0.01)\n",
- " )(actor_output)\n",
- "\n",
- " masked_logits = jnp.where(\n",
- " observation.action_mask,\n",
- " actor_output,\n",
- " jnp.finfo(jnp.float32).min,\n",
- " )\n",
- " \n",
- " return IdentityTransformation(distribution=tfd.Categorical(logits=masked_logits))\n",
- "\n",
- "\n",
- "class Critic(nn.Module):\n",
- " \"\"\"Critic Network.\"\"\"\n",
- "\n",
- " @nn.compact\n",
- " def __call__(self, observation: ObservationGlobalState) -> chex.Array:\n",
- " \"\"\"Forward pass.\"\"\"\n",
- "\n",
- " critic_output = nn.Dense(128, kernel_init=orthogonal(np.sqrt(2)))(\n",
- " observation.global_state\n",
- " )\n",
- " critic_output = nn.relu(critic_output)\n",
- " critic_output = nn.Dense(128, kernel_init=orthogonal(np.sqrt(2)))(\n",
- " critic_output\n",
- " )\n",
- " critic_output = nn.relu(critic_output)\n",
- " critic_output = nn.Dense(1, kernel_init=orthogonal(1.0))(\n",
- " critic_output\n",
- " )\n",
- "\n",
- " return jnp.squeeze(critic_output, axis=-1)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "IFraNFqY6s7_"
- },
- "source": [
- "### Learner Function\n",
- "The `get_learner_fn` function returns a function which produces an `ExperimentOutput`, encapsulating the updated learner state, episode information, and loss metrics."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 3,
- "metadata": {
- "id": "4VVjKmgW64Ct"
- },
- "outputs": [],
- "source": [
- "def get_learner_fn(\n",
- " env: jumanji.Environment,\n",
- " apply_fns: Tuple[ActorApply, CriticApply],\n",
- " update_fns: Tuple[optax.TransformUpdateFn, optax.TransformUpdateFn],\n",
- " config: DictConfig,\n",
- ") -> LearnerFn[LearnerState]:\n",
- " \"\"\"Get the learner function.\"\"\"\n",
- "\n",
- " # Unpack apply and update functions.\n",
- " actor_apply_fn, critic_apply_fn = apply_fns\n",
- " actor_update_fn, critic_update_fn = update_fns\n",
- "\n",
- " def _update_step(learner_state: LearnerState, _: Any) -> Tuple[LearnerState, Tuple]:\n",
- " \"\"\"A single update of the network.\n",
- "\n",
- " This function steps the environment and records the trajectory batch for\n",
- " training. It then calculates advantages and targets based on the recorded\n",
- " trajectory and updates the actor and critic networks based on the calculated\n",
- " losses.\n",
- "\n",
- " Args:\n",
- " learner_state (NamedTuple):\n",
- " - params (Params): The current model parameters.\n",
- " - opt_states (OptStates): The current optimizer states.\n",
- " - key (PRNGKey): The random number generator state.\n",
- " - env_state (State): The environment state.\n",
- " - last_timestep (TimeStep): The last timestep in the current trajectory.\n",
- " _ (Any): The current metrics info.\n",
- " \"\"\"\n",
- "\n",
- " def _env_step(learner_state: LearnerState, _: Any) -> Tuple[LearnerState, PPOTransition]:\n",
- " \"\"\"Step the environment.\"\"\"\n",
- " params, opt_states, key, env_state, last_timestep = learner_state\n",
- "\n",
- " # SELECT ACTION\n",
- " key, policy_key = jax.random.split(key)\n",
- " actor_policy = actor_apply_fn(params.actor_params, last_timestep.observation)\n",
- " value = critic_apply_fn(params.critic_params, last_timestep.observation)\n",
- " action = actor_policy.sample(seed=policy_key)\n",
- " log_prob = actor_policy.log_prob(action)\n",
- "\n",
- " # STEP ENVIRONMENT\n",
- " env_state, timestep = jax.vmap(env.step, in_axes=(0, 0))(env_state, action)\n",
- "\n",
- " # LOG EPISODE METRICS\n",
- " done = jax.tree_util.tree_map(\n",
- " lambda x: jnp.repeat(x, config.system.num_agents).reshape(config.arch.num_envs, -1),\n",
- " timestep.last(),\n",
- " )\n",
- " info = timestep.extras[\"episode_metrics\"]\n",
- "\n",
- " transition = PPOTransition(\n",
- " done, action, value, timestep.reward, log_prob, last_timestep.observation, info\n",
- " )\n",
- " learner_state = LearnerState(params, opt_states, key, env_state, timestep)\n",
- " return learner_state, transition\n",
- "\n",
- " # STEP ENVIRONMENT FOR ROLLOUT LENGTH\n",
- " learner_state, traj_batch = jax.lax.scan(\n",
- " _env_step, learner_state, None, config.system.rollout_length\n",
- " )\n",
- "\n",
- " # CALCULATE ADVANTAGE\n",
- " params, opt_states, key, env_state, last_timestep = learner_state\n",
- " last_val = critic_apply_fn(params.critic_params, last_timestep.observation)\n",
- "\n",
- " def _calculate_gae(\n",
- " traj_batch: PPOTransition, last_val: chex.Array\n",
- " ) -> Tuple[chex.Array, chex.Array]:\n",
- " \"\"\"Calculate the GAE.\"\"\"\n",
- "\n",
- " def _get_advantages(gae_and_next_value: Tuple, transition: PPOTransition) -> Tuple:\n",
- " \"\"\"Calculate the GAE for a single transition.\"\"\"\n",
- " gae, next_value = gae_and_next_value\n",
- " done, value, reward = (\n",
- " transition.done,\n",
- " transition.value,\n",
- " transition.reward,\n",
- " )\n",
- " gamma = config.system.gamma\n",
- " delta = reward + gamma * next_value * (1 - done) - value\n",
- " gae = delta + gamma * config.system.gae_lambda * (1 - done) * gae\n",
- " return (gae, value), gae\n",
- "\n",
- " _, advantages = jax.lax.scan(\n",
- " _get_advantages,\n",
- " (jnp.zeros_like(last_val), last_val),\n",
- " traj_batch,\n",
- " reverse=True,\n",
- " unroll=16,\n",
- " )\n",
- " return advantages, advantages + traj_batch.value\n",
- "\n",
- " advantages, targets = _calculate_gae(traj_batch, last_val)\n",
- "\n",
- " def _update_epoch(update_state: Tuple, _: Any) -> Tuple:\n",
- " \"\"\"Update the network for a single epoch.\"\"\"\n",
- "\n",
- " def _update_minibatch(train_state: Tuple, batch_info: Tuple) -> Tuple:\n",
- " \"\"\"Update the network for a single minibatch.\"\"\"\n",
- "\n",
- " # UNPACK TRAIN STATE AND BATCH INFO\n",
- " params, opt_states, key = train_state\n",
- " traj_batch, advantages, targets = batch_info\n",
- "\n",
- " def _actor_loss_fn(\n",
- " actor_params: FrozenDict,\n",
- " actor_opt_state: OptState,\n",
- " traj_batch: PPOTransition,\n",
- " gae: chex.Array,\n",
- " key: chex.PRNGKey,\n",
- " ) -> Tuple:\n",
- " \"\"\"Calculate the actor loss.\"\"\"\n",
- " # RERUN NETWORK\n",
- " actor_policy = actor_apply_fn(actor_params, traj_batch.obs)\n",
- " log_prob = actor_policy.log_prob(traj_batch.action)\n",
- "\n",
- " # CALCULATE ACTOR LOSS\n",
- " ratio = jnp.exp(log_prob - traj_batch.log_prob)\n",
- " gae = (gae - gae.mean()) / (gae.std() + 1e-8)\n",
- " loss_actor1 = ratio * gae\n",
- " loss_actor2 = (\n",
- " jnp.clip(\n",
- " ratio,\n",
- " 1.0 - config.system.clip_eps,\n",
- " 1.0 + config.system.clip_eps,\n",
- " )\n",
- " * gae\n",
- " )\n",
- " loss_actor = -jnp.minimum(loss_actor1, loss_actor2)\n",
- " loss_actor = loss_actor.mean()\n",
- " # The seed will be used in the TanhTransformedDistribution:\n",
- " entropy = actor_policy.entropy(seed=key).mean()\n",
- "\n",
- " total_loss_actor = loss_actor - config.system.ent_coef * entropy\n",
- " return total_loss_actor, (loss_actor, entropy)\n",
- "\n",
- " def _critic_loss_fn(\n",
- " critic_params: FrozenDict,\n",
- " critic_opt_state: OptState,\n",
- " traj_batch: PPOTransition,\n",
- " targets: chex.Array,\n",
- " ) -> Tuple:\n",
- " \"\"\"Calculate the critic loss.\"\"\"\n",
- " # RERUN NETWORK\n",
- " value = critic_apply_fn(critic_params, traj_batch.obs)\n",
- "\n",
- " # CALCULATE VALUE LOSS\n",
- " value_pred_clipped = traj_batch.value + (value - traj_batch.value).clip(\n",
- " -config.system.clip_eps, config.system.clip_eps\n",
- " )\n",
- " value_losses = jnp.square(value - targets)\n",
- " value_losses_clipped = jnp.square(value_pred_clipped - targets)\n",
- " value_loss = 0.5 * jnp.maximum(value_losses, value_losses_clipped).mean()\n",
- "\n",
- " critic_total_loss = config.system.vf_coef * value_loss\n",
- " return critic_total_loss, (value_loss)\n",
- "\n",
- " # CALCULATE ACTOR LOSS\n",
- " key, entropy_key = jax.random.split(key)\n",
- " actor_grad_fn = jax.value_and_grad(_actor_loss_fn, has_aux=True)\n",
- " actor_loss_info, actor_grads = actor_grad_fn(\n",
- " params.actor_params,\n",
- " opt_states.actor_opt_state,\n",
- " traj_batch,\n",
- " advantages,\n",
- " entropy_key,\n",
- " )\n",
- "\n",
- " # CALCULATE CRITIC LOSS\n",
- " critic_grad_fn = jax.value_and_grad(_critic_loss_fn, has_aux=True)\n",
- " critic_loss_info, critic_grads = critic_grad_fn(\n",
- " params.critic_params, opt_states.critic_opt_state, traj_batch, targets\n",
- " )\n",
- "\n",
- " # Compute the parallel mean (pmean) over the batch.\n",
- " # This calculation is inspired by the Anakin architecture demo notebook.\n",
- " # available at https://tinyurl.com/26tdzs5x\n",
- " # This pmean could be a regular mean as the batch axis is on the same device.\n",
- " actor_grads, actor_loss_info = jax.lax.pmean(\n",
- " (actor_grads, actor_loss_info), axis_name=\"batch\"\n",
- " )\n",
- " # pmean over devices.\n",
- " actor_grads, actor_loss_info = jax.lax.pmean(\n",
- " (actor_grads, actor_loss_info), axis_name=\"device\"\n",
- " )\n",
- "\n",
- " critic_grads, critic_loss_info = jax.lax.pmean(\n",
- " (critic_grads, critic_loss_info), axis_name=\"batch\"\n",
- " )\n",
- " # pmean over devices.\n",
- " critic_grads, critic_loss_info = jax.lax.pmean(\n",
- " (critic_grads, critic_loss_info), axis_name=\"device\"\n",
- " )\n",
- "\n",
- " # UPDATE ACTOR PARAMS AND OPTIMISER STATE\n",
- " actor_updates, actor_new_opt_state = actor_update_fn(\n",
- " actor_grads, opt_states.actor_opt_state\n",
- " )\n",
- " actor_new_params = optax.apply_updates(params.actor_params, actor_updates)\n",
- "\n",
- " # UPDATE CRITIC PARAMS AND OPTIMISER STATE\n",
- " critic_updates, critic_new_opt_state = critic_update_fn(\n",
- " critic_grads, opt_states.critic_opt_state\n",
- " )\n",
- " critic_new_params = optax.apply_updates(params.critic_params, critic_updates)\n",
- "\n",
- " new_params = Params(actor_new_params, critic_new_params)\n",
- " new_opt_state = OptStates(actor_new_opt_state, critic_new_opt_state)\n",
- "\n",
- " # PACK LOSS INFO\n",
- " total_loss = actor_loss_info[0] + critic_loss_info[0]\n",
- " value_loss = critic_loss_info[1]\n",
- " actor_loss = actor_loss_info[1][0]\n",
- " entropy = actor_loss_info[1][1]\n",
- " loss_info = {\n",
- " \"total_loss\": total_loss,\n",
- " \"value_loss\": value_loss,\n",
- " \"actor_loss\": actor_loss,\n",
- " \"entropy\": entropy,\n",
- " }\n",
- " return (new_params, new_opt_state, entropy_key), loss_info\n",
- "\n",
- " params, opt_states, traj_batch, advantages, targets, key = update_state\n",
- " key, shuffle_key, entropy_key = jax.random.split(key, 3)\n",
- "\n",
- " # SHUFFLE MINIBATCHES\n",
- " batch_size = config.system.rollout_length * config.arch.num_envs\n",
- " permutation = jax.random.permutation(shuffle_key, batch_size)\n",
- " batch = (traj_batch, advantages, targets)\n",
- " batch = jax.tree_util.tree_map(lambda x: merge_leading_dims(x, 2), batch)\n",
- " shuffled_batch = jax.tree_util.tree_map(\n",
- " lambda x: jnp.take(x, permutation, axis=0), batch\n",
- " )\n",
- " minibatches = jax.tree_util.tree_map(\n",
- " lambda x: jnp.reshape(x, [config.system.num_minibatches, -1] + list(x.shape[1:])),\n",
- " shuffled_batch,\n",
- " )\n",
- "\n",
- " # UPDATE MINIBATCHES\n",
- " (params, opt_states, entropy_key), loss_info = jax.lax.scan(\n",
- " _update_minibatch, (params, opt_states, entropy_key), minibatches\n",
- " )\n",
- "\n",
- " update_state = (params, opt_states, traj_batch, advantages, targets, key)\n",
- " return update_state, loss_info\n",
- "\n",
- " update_state = (params, opt_states, traj_batch, advantages, targets, key)\n",
- "\n",
- " # UPDATE EPOCHS\n",
- " update_state, loss_info = jax.lax.scan(\n",
- " _update_epoch, update_state, None, config.system.ppo_epochs\n",
- " )\n",
- "\n",
- " params, opt_states, traj_batch, advantages, targets, key = update_state\n",
- " learner_state = LearnerState(params, opt_states, key, env_state, last_timestep)\n",
- " metric = traj_batch.info\n",
- " return learner_state, (metric, loss_info)\n",
- "\n",
- " def learner_fn(learner_state: LearnerState) -> ExperimentOutput[LearnerState]:\n",
- " \"\"\"Learner function.\n",
- "\n",
- " This function represents the learner, it updates the network parameters\n",
- " by iteratively applying the `_update_step` function for a fixed number of\n",
- " updates. The `_update_step` function is vectorized over a batch of inputs.\n",
- "\n",
- " Args:\n",
- " learner_state (NamedTuple):\n",
- " - params (Params): The initial model parameters.\n",
- " - opt_states (OptStates): The initial optimizer states.\n",
- " - key (chex.PRNGKey): The random number generator state.\n",
- " - env_state (LogEnvState): The environment state.\n",
- " - timesteps (TimeStep): The initial timestep in the initial trajectory.\n",
- " \"\"\"\n",
- "\n",
- " batched_update_step = jax.vmap(_update_step, in_axes=(0, None), axis_name=\"batch\")\n",
- "\n",
- " learner_state, (episode_info, loss_info) = jax.lax.scan(\n",
- " batched_update_step, learner_state, None, config.system.num_updates_per_eval\n",
- " )\n",
- " return ExperimentOutput(\n",
- " learner_state=learner_state,\n",
- " episode_metrics=episode_info,\n",
- " train_metrics=loss_info,\n",
- " )\n",
- "\n",
- " return learner_fn"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "4idyWUhW68oS"
- },
- "source": [
- "### Learner Setup\n",
- "The learner setup initialises components for training: the learner function, actor and critic networks and optimizers, environment, and states. It creates a function for learning, employs parallel processing over the cores for efficiency, and sets up initial states."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 4,
- "metadata": {
- "id": "eWjNSGvZ7ALw"
- },
- "outputs": [],
- "source": [
- "def learner_setup(\n",
- " env: jumanji.Environment, keys: chex.Array, config: DictConfig\n",
- ") -> Tuple[LearnerFn[LearnerState], Actor, LearnerState]:\n",
- " \"\"\"Initialise learner_fn, network, optimiser, environment and states.\"\"\"\n",
- " # Get available TPU cores.\n",
- " n_devices = len(jax.devices())\n",
- "\n",
- " # Get number of agents.\n",
- " config.system.num_agents = env.num_agents\n",
- "\n",
- " # PRNG keys.\n",
- " key, actor_net_key, critic_net_key = keys\n",
- " \n",
- " # Define network and optimiser.\n",
- " actor_network = Actor(env.action_dim)\n",
- " critic_network = Critic()\n",
- "\n",
- " actor_lr = make_learning_rate(config.system.actor_lr, config)\n",
- " critic_lr = make_learning_rate(config.system.critic_lr, config)\n",
- "\n",
- " actor_optim = optax.chain(\n",
- " optax.clip_by_global_norm(config.system.max_grad_norm),\n",
- " optax.adam(actor_lr, eps=1e-5),\n",
- " )\n",
- " critic_optim = optax.chain(\n",
- " optax.clip_by_global_norm(config.system.max_grad_norm),\n",
- " optax.adam(critic_lr, eps=1e-5),\n",
- " )\n",
- "\n",
- " # Initialise observation with obs of all agents.\n",
- " obs = env.observation_spec().generate_value()\n",
- " init_x = jax.tree_util.tree_map(lambda x: x[jnp.newaxis, ...], obs)\n",
- "\n",
- " # Initialise actor params and optimiser state.\n",
- " actor_params = actor_network.init(actor_net_key, init_x)\n",
- " actor_opt_state = actor_optim.init(actor_params)\n",
- "\n",
- " # Initialise critic params and optimiser state.\n",
- " critic_params = critic_network.init(critic_net_key, init_x)\n",
- " critic_opt_state = critic_optim.init(critic_params)\n",
- "\n",
- " # Pack params.\n",
- " params = Params(actor_params, critic_params)\n",
- "\n",
- " # Pack apply and update functions.\n",
- " apply_fns = (actor_network.apply, critic_network.apply)\n",
- " update_fns = (actor_optim.update, critic_optim.update)\n",
- "\n",
- " # Get batched iterated update and replicate it to pmap it over cores.\n",
- " learn = get_learner_fn(env, apply_fns, update_fns, config)\n",
- " learn = jax.pmap(learn, axis_name=\"device\")\n",
- "\n",
- " # Initialise environment states and timesteps: across devices and batches.\n",
- " key, *env_keys = jax.random.split(\n",
- " key, n_devices * config.system.update_batch_size * config.arch.num_envs + 1\n",
- " )\n",
- " env_states, timesteps = jax.vmap(env.reset, in_axes=(0))(\n",
- " jnp.stack(env_keys),\n",
- " )\n",
- " reshape_states = lambda x: x.reshape(\n",
- " (n_devices, config.system.update_batch_size, config.arch.num_envs) + x.shape[1:]\n",
- " )\n",
- " # (devices, update batch size, num_envs, ...)\n",
- " env_states = jax.tree_map(reshape_states, env_states)\n",
- " timesteps = jax.tree_map(reshape_states, timesteps)\n",
- "\n",
- " # Define params to be replicated across devices and batches.\n",
- " key, step_keys = jax.random.split(key)\n",
- " opt_states = OptStates(actor_opt_state, critic_opt_state)\n",
- " replicate_learner = (params, opt_states, step_keys)\n",
- "\n",
- " # Duplicate learner for update_batch_size.\n",
- " broadcast = lambda x: jnp.broadcast_to(x, (config.system.update_batch_size,) + x.shape)\n",
- " replicate_learner = jax.tree_map(broadcast, replicate_learner)\n",
- "\n",
- " # Duplicate learner across devices.\n",
- " replicate_learner = flax.jax_utils.replicate(replicate_learner, devices=jax.devices())\n",
- "\n",
- " # Initialise learner state.\n",
- " params, opt_states, step_keys = replicate_learner\n",
- " init_learner_state = LearnerState(params, opt_states, step_keys, env_states, timesteps)\n",
- "\n",
- " return learn, actor_network, init_learner_state\n"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "GefUs8Yd7EJt"
- },
- "source": [
- "# Rendering and logging tools"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "Vkd95_VpYJf0"
- },
- "source": [
- "### Rendering\n",
- "The `render_one_episode` function simulates and visualises one episode from rolling out a trained MAPPO model that will be passed to the function using `actors_params`."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 5,
- "metadata": {
- "id": "DU7OVSm6HM6q"
- },
- "outputs": [],
- "source": [
- "def render_one_episode(config, params, max_steps=100) -> None:\n",
- " \"\"\"Rollout episdoes of a trained MAPPO.\"\"\"\n",
- " # Create envs\n",
- " env = jumanji.make(config[\"env\"][\"env_name\"])\n",
- " env = RwareWrapper(env, add_global_state=True)\n",
- " # Add agent id to observation.\n",
- " if config[\"system\"][\"add_agent_id\"]:\n",
- " env = AgentIDWrapper(env=env)\n",
- "\n",
- " # Create actor networks (We only care about the policy during the rendering)\n",
- " actor_network = Actor(env.action_dim)\n",
- " apply_fn = actor_network.apply\n",
- " \n",
- " reset_fn = jax.jit(env.reset)\n",
- " step_fn = jax.jit(env.step)\n",
- " key = jax.random.PRNGKey(config.system.seed)\n",
- " key, reset_key = jax.random.split(key)\n",
- " state, timestep = reset_fn(reset_key)\n",
- " \n",
- " states=[state]\n",
- " episode_return = 0\n",
- " episode_length = 0\n",
- " while not timestep.last():\n",
- " key, action_key = jax.random.split(key)\n",
- " pi = apply_fn(params, timestep.observation)\n",
- "\n",
- " if config[\"arch\"][\"evaluation_greedy\"]:\n",
- " action = pi.mode()\n",
- " else:\n",
- " action = pi.sample(seed=action_key)\n",
- " state, timestep = step_fn(state, action)\n",
- " states.append(state)\n",
- " episode_return+= jnp.mean(timestep.reward)\n",
- " episode_length+=1\n",
- "\n",
- " # Print out the results of the episode\n",
- " print(f\"{Fore.CYAN}{Style.BRIGHT}EPISODE RETURN: {episode_return}{Style.RESET_ALL}\")\n",
- " print(f\"{Fore.CYAN}{Style.BRIGHT}EPISODE LENGTH:{episode_length}{Style.RESET_ALL}\")\n",
- "\n",
- " # Limit the number of steps to record to the maximum number of steps\n",
- " steps=min([max_steps,len(states)-1])\n",
- " states=states[:steps]\n",
- "\n",
- " # Render the episode\n",
- " env.animate(states=states, interval=100, save_path=\"./rware.gif\")"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "N2pDHF1Q8Cn7"
- },
- "source": [
- "### Logging:\n",
- "The `plot_performance` function visualises the performance of the algorithm, this plot will be refreshed each time evaluation interval happens!"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 6,
- "metadata": {
- "id": "OwkZqb8y8GYG"
- },
- "outputs": [],
- "source": [
- "def plot_performance(episode_metrics, ep_returns, start_time):\n",
- " plt.figure(figsize=(8, 4))\n",
- " clear_output(wait=True)\n",
- " \n",
- " # Plot the data\n",
- " ep_returns.append(episode_metrics[\"episode_return\"].mean())\n",
- " plt.plot(np.linspace(0, (time.time()-start_time)/ 60.0, len(list(ep_returns))),list(ep_returns))\n",
- " plt.xlabel('Run Time [Minutes]')\n",
- " plt.ylabel('Episode Return')\n",
- " plt.title('Robotic Warehouse with 4 Agents')\n",
- " \n",
- " # Show the plot\n",
- " plt.show()\n",
- " return ep_returns"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "GLLqQgn1754J"
- },
- "source": [
- "# Experiment Setup"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "CwFYuKpfZyx9"
- },
- "source": [
- "The experiment setup includes: defining the hyperparameters, creating environments, setting up the learner and evaluator, and initialising some variables for plotting and logging purposes.\n"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "OHgQbXfY8LqF"
- },
- "source": [
- "#### Config\n",
- "\n",
- "We start the experiment setup by defining the config dictionary that represents a set of the various hyperparameters for the experiment."
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "S7LZsJW3K_xH"
- },
- "source": [
- "In addition to the typical hyperparameters used in MARL algorithms, we define below a few variables relevant to our implementation:\n",
- "\n",
- "`num_updates`: The number of gradient updates to perform during the training.\n",
- "\n",
- "`num_envs`: Number of vectorised environments per device. For instance, if set to 512, it implies that 512 environments will be running in parallel at the same time on a given process.\n",
- "\n",
- "`num_evaluation` and `num_eval_episodes`: The `num_evaluation` parameter specifies how many evenly spaced evaluation steps will occur during training, while the `num_eval_episode` specifies how many episodes will be rolled out at each evaluation step."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 11,
- "metadata": {
- "id": "wexJ0Slr8INC"
- },
- "outputs": [],
- "source": [
- "config = { \"system\": {\n",
- " \"actor_lr\": 2.5e-4,\n",
- " \"critic_lr\": 2.5e-4,\n",
- " \"update_batch_size\": 2,\n",
- " \"rollout_length\": 128,\n",
- " \"num_updates\": 400,\n",
- " \"ppo_epochs\": 16,\n",
- " \"num_minibatches\": 32,\n",
- " \"gamma\": 0.99,\n",
- " \"gae_lambda\": 0.95,\n",
- " \"clip_eps\": 0.2,\n",
- " \"ent_coef\": 0.01,\n",
- " \"vf_coef\": 0.5,\n",
- " \"max_grad_norm\": 0.5,\n",
- " \"add_agent_id\": True,\n",
- " \"decay_learning_rates\": False,\n",
- " \"seed\":42\n",
- " },\n",
- " \"arch\": {\n",
- " \"num_envs\": 512,\n",
- " \"num_eval_episodes\": 32,\n",
- " \"num_evaluation\": 50,\n",
- " \"evaluation_greedy\": False,\n",
- " },\n",
- " \"env\":{\n",
- " \"env_name\": \"RobotWarehouse-v0\",\n",
- " \"eval_metric\": \"episode_return\",\n",
- " \"log_win_rate\": False,\n",
- " }\n",
- " }\n",
- "# Convert the Python dictionary to a DictConfig\n",
- "config: DictConfig = OmegaConf.create(config)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "sub4CAfrLHbM"
- },
- "source": [
- "#### Define Training and Evaluation environments"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "dwMHRotOLmdT"
- },
- "source": [
- "We use a series of wrappers to configure the training and evaluation environments, each with distinct purposes, described as follows:\n",
- "\n",
- "`RwareWrapper`: A wrapper for training and evaluating the environment of a robotic warehouse using the Mava system.\n",
- "\n",
- "`GlobalStateWrapper`: This wrapper includes a global environment state to be used by the centralised critic. It's worth noting that since robotic warehouse does not have a global state, we create one by concatenating the observations of all agents.\n",
- "\n",
- "`AutoResetWrapper`: This wrapper automatically resets the environment after a completed episode. Once a terminal state is attained, the state, observation, and step type are reset in readiness for subsequent interactions.\n",
- "\n",
- "`RecordEpisodeMetrics`: This wrapper contributes to the logging process by capturing episode returns and lengths during the episode step invocation.\n",
- "\n",
- "`AgentIDWrapper`: This wrapper adds one-hot agent IDs to agent observations."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 8,
- "metadata": {
- "id": "lCqZohi0vKSR"
- },
- "outputs": [],
- "source": [
- "# Set up a Jumanji environment for training.\n",
- "env = jumanji.make(config[\"env\"][\"env_name\"])\n",
- "env = RwareWrapper(env, add_global_state=True)\n",
- "\n",
- "# Set up a Jumanji environment for evaluation.\n",
- "eval_env = jumanji.make(config[\"env\"][\"env_name\"])\n",
- "eval_env = RwareWrapper(eval_env, add_global_state=True)\n",
- "\n",
- "# Add agent id to observation.\n",
- "if config[\"system\"][\"add_agent_id\"]:\n",
- " env = AgentIDWrapper(env=env)\n",
- " eval_env = AgentIDWrapper(env=eval_env)\n",
- "env = AutoResetWrapper(env)\n",
- "env = RecordEpisodeMetrics(env)\n"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "PrFx-V-DNUkN"
- },
- "source": [
- "#### The Learner and Evaluator Setup\n",
- "\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 12,
- "metadata": {
- "id": "gp-FLgLSNg29"
- },
- "outputs": [],
- "source": [
- "# PRNG keys.\n",
- "key, key_e, actor_net_key, critic_net_key = jax.random.split( jax.random.PRNGKey(config.system.seed), num=4)\n",
- "\n",
- "# Setup learner.\n",
- "learn, actor_network, learner_state = learner_setup( env, (key, actor_net_key, critic_net_key), config)\n",
- "\n",
- "# Setup evaluator.\n",
- "evaluator, absolute_metric_evaluator = make_eval_fns(eval_env, actor_network.apply, config, use_recurrent_net=False)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "mCyGr8_bNurn"
- },
- "source": [
- "#### Additional variable definitions\n"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "tZm3mf5uxo5m"
- },
- "source": [
- "In this section of the code, the total number of timesteps for the experiment is calculated, followed by the partitioning of the training timesteps into distinct intervals determined by the value of `num_evaluation`.\n",
- "\n",
- "**Calculating Total Timesteps:**\n",
- "\n",
- "To calculate the total timesteps, the following formula is used:\n",
- "```\n",
- "total_timesteps = n_devices\n",
- "* num_updates\n",
- "* rollout_length\n",
- "* update_batch_size\n",
- "* num_envs\n",
- "```\n",
- "- `n_devices` represents the number of JAX devices available, which is essential for parallel computation.\n",
- "- `num_updates` is the number of vectorised gradient updates to be be performed on each device.\n",
- "- `rollout_length` is the number of timesteps in each rollout.\n",
- "- `update_batch_size` is the batch size used for each update.\n",
- "- `num_envs` is the number of parallel environments used for data collection.\n",
- "\n",
- "This computation yields the total count of timesteps that will be carried out throughout the complete training procedure. Consequently, the number of timesteps within each training interval is established as ```total_timesteps/num_evaluation```\n",
- "\n",
- "\n",
- "\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 13,
- "metadata": {
- "id": "XeqzRKVPxP2F"
- },
- "outputs": [],
- "source": [
- "# Calculate total timesteps.\n",
- "n_devices = len(jax.devices())\n",
- "config[\"system\"][\"num_updates_per_eval\"] = config[\"system\"][\"num_updates\"] // config[\"arch\"][\"num_evaluation\"]\n",
- "steps_per_rollout = (\n",
- " n_devices\n",
- " * config[\"system\"][\"num_updates_per_eval\"]\n",
- " * config[\"system\"][\"rollout_length\"]\n",
- " * config[\"system\"][\"update_batch_size\"]\n",
- " * config[\"arch\"][\"num_envs\"]\n",
- ")\n",
- "\n",
- "# Run experiment for a total number of evaluations.\n",
- "ep_returns=[]"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "OTMzsQEOa4Bv"
- },
- "source": [
- "# Run Experiment"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "gPHBA2aU1jnC"
- },
- "source": [
- "#### Execute the experiment"
- ]
- },
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "8uCEQLS3zZUn"
+ },
+ "source": [
+ "# Mava Quickstart Notebook\n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "0Ajb_J4W1u4V"
+ },
+ "source": [
+ ""
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "a99IjmO51uP2"
+ },
+ "source": [
+ "### This notebook offers a simple introduction to [Mava](https://github.com/instadeepai/Mava) by showing how to build and train a multi-agent PPO (MAPPO) system on the RobotWarehouse environment from [Jumanji](https://github.com/instadeepai/jumanji). Mava follows the design philosophy of [CleanRL](https://github.com/vwxyzjn/cleanrl) allowing for easy code readability and reuse, and is built on top of code from [PureJaxRL](https://github.com/luchris429/purejaxrl), extending it to provide end-to-end JAX-based multi-agent algorithms.\n",
+ "\n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "LYmyi-lU3a-b"
+ },
+ "source": [
+ "# Requirements\n",
+ "\n",
+ "We start by installing and importing the necessary packages."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "cellView": "form",
+ "id": "5l-eEkH-2f0D"
+ },
+ "outputs": [],
+ "source": [
+ "%%capture\n",
+ "# @title Install Mava\n",
+ "! pip install git+https://github.com/instadeepai/mava.git@develop\n",
+ "! pip install \"jax[cuda12_pip]\" -f https://storage.googleapis.com/jax-releases/jax_cuda_releases.html"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "IMBnurbl-9Ez"
+ },
+ "source": [
+ "Restarting the runtime is necessary after reinstalling JAX in Colab to ensure that the changes take effect and that the runtime environment is properly configured for the updated JAX version."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "cellView": "form",
+ "id": "2pMV4rGjTQAw"
+ },
+ "outputs": [],
+ "source": [
+ "# @title Restart Google Colab runtime\n",
+ "import os\n",
+ "\n",
+ "os.kill(os.getpid(), 9)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {
+ "cellView": "form",
+ "id": "FjXA8JyI1_YW"
+ },
+ "outputs": [
{
- "cell_type": "markdown",
- "metadata": {
- "id": "SOMJZaDGbx8P"
- },
- "source": [
- "Now that the code has been compiled using JAX, its execution will benefit from optimised performance. We will proceed to train the MAPPO algorithm on the `small-4ag-easy` scenario from RobotWarehouse. The experiment follows a cyclic pattern, transitioning from training to evaluation and back to training.\n",
- "\n",
- "The training phase consists of performing 400 updates. Each update utilizes 512 parallel environments, with a rollout of 128 steps per environment and a batch of two vectorised full gradient update steps are performend. This comprehensive process results in over 50 million timesteps utilised for training."
- ]
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "/home/wiemkhlifi/miniconda3/envs/mava/lib/python3.9/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
+ " from .autonotebook import tqdm as notebook_tqdm\n"
+ ]
},
{
- "cell_type": "code",
- "execution_count": 14,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "image/png": 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",
- "text/plain": [
- ""
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\u001b[36m\u001b[1mMAPPO experiment completed\u001b[0m\n"
- ]
- }
+ "data": {
+ "text/html": [
+ ""
],
- "source": [
- "start_time=time.time()\n",
- "for _ in range(config[\"arch\"][\"num_evaluation\"]):\n",
- " # Train.\n",
- " learner_output = learn(learner_state)\n",
- " jax.block_until_ready(learner_output)\n",
- " \n",
- " # Prepare for evaluation.\n",
- " trained_params = unreplicate_batch_dim(learner_state.params.actor_params)\n",
- "\n",
- " key_e, *eval_keys = jax.random.split(key_e, n_devices + 1)\n",
- " eval_keys = jnp.stack(eval_keys)\n",
- " eval_keys = eval_keys.reshape(n_devices, -1)\n",
- "\n",
- " # Evaluate.\n",
- " evaluator_output = evaluator(trained_params, eval_keys)\n",
- " jax.block_until_ready(evaluator_output)\n",
- " ep_returns=plot_performance(evaluator_output.episode_metrics, ep_returns, start_time)\n",
- "\n",
- " # Update runner state to continue training.\n",
- " learner_state = learner_output.learner_state\n",
- "\n",
- "# Return trained params to be used for rendering or testing.\n",
- "trained_params= unreplicate_n_dims(trained_params, unreplicate_depth=1)\n",
- "\n",
- "print(f\"{Fore.CYAN}{Style.BRIGHT}MAPPO experiment completed{Style.RESET_ALL}\")"
+ "text/plain": [
+ "[(0.00392156862745098, 0.45098039215686275, 0.6980392156862745),\n",
+ " (0.8705882352941177, 0.5607843137254902, 0.0196078431372549),\n",
+ " (0.00784313725490196, 0.6196078431372549, 0.45098039215686275),\n",
+ " (0.8352941176470589, 0.3686274509803922, 0.0),\n",
+ " (0.8, 0.47058823529411764, 0.7372549019607844),\n",
+ " (0.792156862745098, 0.5686274509803921, 0.3803921568627451),\n",
+ " (0.984313725490196, 0.6862745098039216, 0.8941176470588236),\n",
+ " (0.5803921568627451, 0.5803921568627451, 0.5803921568627451),\n",
+ " (0.9254901960784314, 0.8823529411764706, 0.2),\n",
+ " (0.33725490196078434, 0.7058823529411765, 0.9137254901960784)]"
]
+ },
+ "execution_count": 1,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# @title Import required packages.\n",
+ "\n",
+ "import time\n",
+ "from typing import Any, Sequence, Tuple\n",
+ "\n",
+ "import chex\n",
+ "import flax\n",
+ "import flax.linen as nn\n",
+ "import jax\n",
+ "import jax.numpy as jnp\n",
+ "\n",
+ "# Env requirements\n",
+ "import jumanji\n",
+ "\n",
+ "# Plot requirements\n",
+ "import matplotlib.pyplot as plt\n",
+ "import numpy as np\n",
+ "import optax\n",
+ "import tensorflow_probability.substrates.jax.distributions as tfd\n",
+ "from colorama import Fore, Style\n",
+ "from flax.core.frozen_dict import FrozenDict\n",
+ "from flax.linen.initializers import orthogonal\n",
+ "from IPython.display import clear_output\n",
+ "from omegaconf import DictConfig, OmegaConf\n",
+ "from optax._src.base import OptState\n",
+ "\n",
+ "# Mava Helpful functions and types\n",
+ "from mava.distributions import IdentityTransformation\n",
+ "from mava.evaluator import make_eval_fns\n",
+ "from mava.systems.ppo.types import LearnerState, OptStates, Params, PPOTransition\n",
+ "from mava.types import (\n",
+ " ActorApply,\n",
+ " CriticApply,\n",
+ " ExperimentOutput,\n",
+ " LearnerFn,\n",
+ " Observation,\n",
+ " ObservationGlobalState,\n",
+ ")\n",
+ "from mava.utils.jax_utils import (\n",
+ " merge_leading_dims,\n",
+ " unreplicate_batch_dim,\n",
+ " unreplicate_n_dims,\n",
+ ")\n",
+ "from mava.utils.training import make_learning_rate\n",
+ "from mava.wrappers import (\n",
+ " AgentIDWrapper,\n",
+ " AutoResetWrapper,\n",
+ " RecordEpisodeMetrics,\n",
+ " RwareWrapper,\n",
+ ")\n",
+ "\n",
+ "%matplotlib inline\n",
+ "import seaborn as sns\n",
+ "\n",
+ "sns.set()\n",
+ "sns.set_style(\"white\")\n",
+ "sns.color_palette(\"colorblind\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "9omksZSH6htZ"
+ },
+ "source": [
+ "# Trainer\n",
+ "This section encompasses the foundational methods required to set up the training process for MAPPO.\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "JaIw_5YaUSAB"
+ },
+ "source": [
+ "### Network\n",
+ "\n",
+ "Initially, we start by constructing the Actor and Critic networks using components from the Flax library.\n",
+ "\n",
+ "* The `Actor()` network takes an observation as input and produces logits representing the probabilities of different actions. The shapes within the network are determined dynamically based on the number of agents, the observation, and the batch size.\n",
+ "* The `Critic()` network takes the global state as input and produces the estimated value of the state. Similar to the Actor network, the shapes within the network are handled implicitly by Flax."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
},
+ "id": "Sss6opmC6lmp",
+ "outputId": "7eb3833d-d44b-4218-c9f4-aae8aa2e447c"
+ },
+ "outputs": [],
+ "source": [
+ "class Actor(nn.Module):\n",
+ " \"\"\"Actor Network.\"\"\"\n",
+ "\n",
+ " action_dim: Sequence[int]\n",
+ "\n",
+ " @nn.compact\n",
+ " def __call__(self, observation: Observation) -> tfd.TransformedDistribution:\n",
+ " \"\"\"Forward pass.\"\"\"\n",
+ " x = observation.agents_view\n",
+ "\n",
+ " actor_output = nn.Dense(128, kernel_init=orthogonal(np.sqrt(2)))(x)\n",
+ " actor_output = nn.relu(actor_output)\n",
+ " actor_output = nn.Dense(128, kernel_init=orthogonal(np.sqrt(2)))(actor_output)\n",
+ " actor_output = nn.relu(actor_output)\n",
+ " actor_output = nn.Dense(self.action_dim, kernel_init=orthogonal(0.01))(actor_output)\n",
+ "\n",
+ " masked_logits = jnp.where(\n",
+ " observation.action_mask,\n",
+ " actor_output,\n",
+ " jnp.finfo(jnp.float32).min,\n",
+ " )\n",
+ "\n",
+ " return IdentityTransformation(distribution=tfd.Categorical(logits=masked_logits))\n",
+ "\n",
+ "\n",
+ "class Critic(nn.Module):\n",
+ " \"\"\"Critic Network.\"\"\"\n",
+ "\n",
+ " @nn.compact\n",
+ " def __call__(self, observation: ObservationGlobalState) -> chex.Array:\n",
+ " \"\"\"Forward pass.\"\"\"\n",
+ " critic_output = nn.Dense(128, kernel_init=orthogonal(np.sqrt(2)))(observation.global_state)\n",
+ " critic_output = nn.relu(critic_output)\n",
+ " critic_output = nn.Dense(128, kernel_init=orthogonal(np.sqrt(2)))(critic_output)\n",
+ " critic_output = nn.relu(critic_output)\n",
+ " critic_output = nn.Dense(1, kernel_init=orthogonal(1.0))(critic_output)\n",
+ "\n",
+ " return jnp.squeeze(critic_output, axis=-1)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "IFraNFqY6s7_"
+ },
+ "source": [
+ "### Learner Function\n",
+ "The `get_learner_fn` function returns a function which produces an `ExperimentOutput`, encapsulating the updated learner state, episode information, and loss metrics."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {
+ "id": "4VVjKmgW64Ct"
+ },
+ "outputs": [],
+ "source": [
+ "def get_learner_fn(\n",
+ " env: jumanji.Environment,\n",
+ " apply_fns: Tuple[ActorApply, CriticApply],\n",
+ " update_fns: Tuple[optax.TransformUpdateFn, optax.TransformUpdateFn],\n",
+ " config: DictConfig,\n",
+ ") -> LearnerFn[LearnerState]:\n",
+ " \"\"\"Get the learner function.\"\"\"\n",
+ " # Unpack apply and update functions.\n",
+ " actor_apply_fn, critic_apply_fn = apply_fns\n",
+ " actor_update_fn, critic_update_fn = update_fns\n",
+ "\n",
+ " def _update_step(learner_state: LearnerState, _: Any) -> Tuple[LearnerState, Tuple]:\n",
+ " \"\"\"A single update of the network.\n",
+ "\n",
+ " This function steps the environment and records the trajectory batch for\n",
+ " training. It then calculates advantages and targets based on the recorded\n",
+ " trajectory and updates the actor and critic networks based on the calculated\n",
+ " losses.\n",
+ "\n",
+ " Args:\n",
+ " ----\n",
+ " learner_state (NamedTuple):\n",
+ " - params (Params): The current model parameters.\n",
+ " - opt_states (OptStates): The current optimizer states.\n",
+ " - key (PRNGKey): The random number generator state.\n",
+ " - env_state (State): The environment state.\n",
+ " - last_timestep (TimeStep): The last timestep in the current trajectory.\n",
+ " _ (Any): The current metrics info.\n",
+ "\n",
+ " \"\"\"\n",
+ "\n",
+ " def _env_step(learner_state: LearnerState, _: Any) -> Tuple[LearnerState, PPOTransition]:\n",
+ " \"\"\"Step the environment.\"\"\"\n",
+ " params, opt_states, key, env_state, last_timestep = learner_state\n",
+ "\n",
+ " # SELECT ACTION\n",
+ " key, policy_key = jax.random.split(key)\n",
+ " actor_policy = actor_apply_fn(params.actor_params, last_timestep.observation)\n",
+ " value = critic_apply_fn(params.critic_params, last_timestep.observation)\n",
+ " action = actor_policy.sample(seed=policy_key)\n",
+ " log_prob = actor_policy.log_prob(action)\n",
+ "\n",
+ " # STEP ENVIRONMENT\n",
+ " env_state, timestep = jax.vmap(env.step, in_axes=(0, 0))(env_state, action)\n",
+ "\n",
+ " # LOG EPISODE METRICS\n",
+ " done = jax.tree_util.tree_map(\n",
+ " lambda x: jnp.repeat(x, config.system.num_agents).reshape(config.arch.num_envs, -1),\n",
+ " timestep.last(),\n",
+ " )\n",
+ " info = timestep.extras[\"episode_metrics\"]\n",
+ "\n",
+ " transition = PPOTransition(\n",
+ " done,\n",
+ " action,\n",
+ " value,\n",
+ " timestep.reward,\n",
+ " log_prob,\n",
+ " last_timestep.observation,\n",
+ " info,\n",
+ " )\n",
+ " learner_state = LearnerState(params, opt_states, key, env_state, timestep)\n",
+ " return learner_state, transition\n",
+ "\n",
+ " # STEP ENVIRONMENT FOR ROLLOUT LENGTH\n",
+ " learner_state, traj_batch = jax.lax.scan(\n",
+ " _env_step, learner_state, None, config.system.rollout_length\n",
+ " )\n",
+ "\n",
+ " # CALCULATE ADVANTAGE\n",
+ " params, opt_states, key, env_state, last_timestep = learner_state\n",
+ " last_val = critic_apply_fn(params.critic_params, last_timestep.observation)\n",
+ "\n",
+ " def _calculate_gae(\n",
+ " traj_batch: PPOTransition, last_val: chex.Array\n",
+ " ) -> Tuple[chex.Array, chex.Array]:\n",
+ " \"\"\"Calculate the GAE.\"\"\"\n",
+ "\n",
+ " def _get_advantages(gae_and_next_value: Tuple, transition: PPOTransition) -> Tuple:\n",
+ " \"\"\"Calculate the GAE for a single transition.\"\"\"\n",
+ " gae, next_value = gae_and_next_value\n",
+ " done, value, reward = (\n",
+ " transition.done,\n",
+ " transition.value,\n",
+ " transition.reward,\n",
+ " )\n",
+ " gamma = config.system.gamma\n",
+ " delta = reward + gamma * next_value * (1 - done) - value\n",
+ " gae = delta + gamma * config.system.gae_lambda * (1 - done) * gae\n",
+ " return (gae, value), gae\n",
+ "\n",
+ " _, advantages = jax.lax.scan(\n",
+ " _get_advantages,\n",
+ " (jnp.zeros_like(last_val), last_val),\n",
+ " traj_batch,\n",
+ " reverse=True,\n",
+ " unroll=16,\n",
+ " )\n",
+ " return advantages, advantages + traj_batch.value\n",
+ "\n",
+ " advantages, targets = _calculate_gae(traj_batch, last_val)\n",
+ "\n",
+ " def _update_epoch(update_state: Tuple, _: Any) -> Tuple:\n",
+ " \"\"\"Update the network for a single epoch.\"\"\"\n",
+ "\n",
+ " def _update_minibatch(train_state: Tuple, batch_info: Tuple) -> Tuple:\n",
+ " \"\"\"Update the network for a single minibatch.\"\"\"\n",
+ " # UNPACK TRAIN STATE AND BATCH INFO\n",
+ " params, opt_states, key = train_state\n",
+ " traj_batch, advantages, targets = batch_info\n",
+ "\n",
+ " def _actor_loss_fn(\n",
+ " actor_params: FrozenDict,\n",
+ " actor_opt_state: OptState,\n",
+ " traj_batch: PPOTransition,\n",
+ " gae: chex.Array,\n",
+ " key: chex.PRNGKey,\n",
+ " ) -> Tuple:\n",
+ " \"\"\"Calculate the actor loss.\"\"\"\n",
+ " # RERUN NETWORK\n",
+ " actor_policy = actor_apply_fn(actor_params, traj_batch.obs)\n",
+ " log_prob = actor_policy.log_prob(traj_batch.action)\n",
+ "\n",
+ " # CALCULATE ACTOR LOSS\n",
+ " ratio = jnp.exp(log_prob - traj_batch.log_prob)\n",
+ " gae = (gae - gae.mean()) / (gae.std() + 1e-8)\n",
+ " loss_actor1 = ratio * gae\n",
+ " loss_actor2 = (\n",
+ " jnp.clip(\n",
+ " ratio,\n",
+ " 1.0 - config.system.clip_eps,\n",
+ " 1.0 + config.system.clip_eps,\n",
+ " )\n",
+ " * gae\n",
+ " )\n",
+ " loss_actor = -jnp.minimum(loss_actor1, loss_actor2)\n",
+ " loss_actor = loss_actor.mean()\n",
+ " # The seed will be used in the TanhTransformedDistribution:\n",
+ " entropy = actor_policy.entropy(seed=key).mean()\n",
+ "\n",
+ " total_loss_actor = loss_actor - config.system.ent_coef * entropy\n",
+ " return total_loss_actor, (loss_actor, entropy)\n",
+ "\n",
+ " def _critic_loss_fn(\n",
+ " critic_params: FrozenDict,\n",
+ " critic_opt_state: OptState,\n",
+ " traj_batch: PPOTransition,\n",
+ " targets: chex.Array,\n",
+ " ) -> Tuple:\n",
+ " \"\"\"Calculate the critic loss.\"\"\"\n",
+ " # RERUN NETWORK\n",
+ " value = critic_apply_fn(critic_params, traj_batch.obs)\n",
+ "\n",
+ " # CALCULATE VALUE LOSS\n",
+ " value_pred_clipped = traj_batch.value + (value - traj_batch.value).clip(\n",
+ " -config.system.clip_eps, config.system.clip_eps\n",
+ " )\n",
+ " value_losses = jnp.square(value - targets)\n",
+ " value_losses_clipped = jnp.square(value_pred_clipped - targets)\n",
+ " value_loss = 0.5 * jnp.maximum(value_losses, value_losses_clipped).mean()\n",
+ "\n",
+ " critic_total_loss = config.system.vf_coef * value_loss\n",
+ " return critic_total_loss, (value_loss)\n",
+ "\n",
+ " # CALCULATE ACTOR LOSS\n",
+ " key, entropy_key = jax.random.split(key)\n",
+ " actor_grad_fn = jax.value_and_grad(_actor_loss_fn, has_aux=True)\n",
+ " actor_loss_info, actor_grads = actor_grad_fn(\n",
+ " params.actor_params,\n",
+ " opt_states.actor_opt_state,\n",
+ " traj_batch,\n",
+ " advantages,\n",
+ " entropy_key,\n",
+ " )\n",
+ "\n",
+ " # CALCULATE CRITIC LOSS\n",
+ " critic_grad_fn = jax.value_and_grad(_critic_loss_fn, has_aux=True)\n",
+ " critic_loss_info, critic_grads = critic_grad_fn(\n",
+ " params.critic_params,\n",
+ " opt_states.critic_opt_state,\n",
+ " traj_batch,\n",
+ " targets,\n",
+ " )\n",
+ "\n",
+ " # Compute the parallel mean (pmean) over the batch.\n",
+ " # This calculation is inspired by the Anakin architecture demo notebook.\n",
+ " # available at https://tinyurl.com/26tdzs5x\n",
+ " # This pmean could be a regular mean as the batch axis is on the same device.\n",
+ " actor_grads, actor_loss_info = jax.lax.pmean(\n",
+ " (actor_grads, actor_loss_info), axis_name=\"batch\"\n",
+ " )\n",
+ " # pmean over devices.\n",
+ " actor_grads, actor_loss_info = jax.lax.pmean(\n",
+ " (actor_grads, actor_loss_info), axis_name=\"device\"\n",
+ " )\n",
+ "\n",
+ " critic_grads, critic_loss_info = jax.lax.pmean(\n",
+ " (critic_grads, critic_loss_info), axis_name=\"batch\"\n",
+ " )\n",
+ " # pmean over devices.\n",
+ " critic_grads, critic_loss_info = jax.lax.pmean(\n",
+ " (critic_grads, critic_loss_info), axis_name=\"device\"\n",
+ " )\n",
+ "\n",
+ " # UPDATE ACTOR PARAMS AND OPTIMISER STATE\n",
+ " actor_updates, actor_new_opt_state = actor_update_fn(\n",
+ " actor_grads, opt_states.actor_opt_state\n",
+ " )\n",
+ " actor_new_params = optax.apply_updates(params.actor_params, actor_updates)\n",
+ "\n",
+ " # UPDATE CRITIC PARAMS AND OPTIMISER STATE\n",
+ " critic_updates, critic_new_opt_state = critic_update_fn(\n",
+ " critic_grads, opt_states.critic_opt_state\n",
+ " )\n",
+ " critic_new_params = optax.apply_updates(params.critic_params, critic_updates)\n",
+ "\n",
+ " new_params = Params(actor_new_params, critic_new_params)\n",
+ " new_opt_state = OptStates(actor_new_opt_state, critic_new_opt_state)\n",
+ "\n",
+ " # PACK LOSS INFO\n",
+ " total_loss = actor_loss_info[0] + critic_loss_info[0]\n",
+ " value_loss = critic_loss_info[1]\n",
+ " actor_loss = actor_loss_info[1][0]\n",
+ " entropy = actor_loss_info[1][1]\n",
+ " loss_info = {\n",
+ " \"total_loss\": total_loss,\n",
+ " \"value_loss\": value_loss,\n",
+ " \"actor_loss\": actor_loss,\n",
+ " \"entropy\": entropy,\n",
+ " }\n",
+ " return (new_params, new_opt_state, entropy_key), loss_info\n",
+ "\n",
+ " params, opt_states, traj_batch, advantages, targets, key = update_state\n",
+ " key, shuffle_key, entropy_key = jax.random.split(key, 3)\n",
+ "\n",
+ " # SHUFFLE MINIBATCHES\n",
+ " batch_size = config.system.rollout_length * config.arch.num_envs\n",
+ " permutation = jax.random.permutation(shuffle_key, batch_size)\n",
+ " batch = (traj_batch, advantages, targets)\n",
+ " batch = jax.tree_util.tree_map(lambda x: merge_leading_dims(x, 2), batch)\n",
+ " shuffled_batch = jax.tree_util.tree_map(\n",
+ " lambda x: jnp.take(x, permutation, axis=0), batch\n",
+ " )\n",
+ " minibatches = jax.tree_util.tree_map(\n",
+ " lambda x: jnp.reshape(x, [config.system.num_minibatches, -1] + list(x.shape[1:])),\n",
+ " shuffled_batch,\n",
+ " )\n",
+ "\n",
+ " # UPDATE MINIBATCHES\n",
+ " (params, opt_states, entropy_key), loss_info = jax.lax.scan(\n",
+ " _update_minibatch, (params, opt_states, entropy_key), minibatches\n",
+ " )\n",
+ "\n",
+ " update_state = (params, opt_states, traj_batch, advantages, targets, key)\n",
+ " return update_state, loss_info\n",
+ "\n",
+ " update_state = (params, opt_states, traj_batch, advantages, targets, key)\n",
+ "\n",
+ " # UPDATE EPOCHS\n",
+ " update_state, loss_info = jax.lax.scan(\n",
+ " _update_epoch, update_state, None, config.system.ppo_epochs\n",
+ " )\n",
+ "\n",
+ " params, opt_states, traj_batch, advantages, targets, key = update_state\n",
+ " learner_state = LearnerState(params, opt_states, key, env_state, last_timestep)\n",
+ " metric = traj_batch.info\n",
+ " return learner_state, (metric, loss_info)\n",
+ "\n",
+ " def learner_fn(learner_state: LearnerState) -> ExperimentOutput[LearnerState]:\n",
+ " \"\"\"Learner function.\n",
+ "\n",
+ " This function represents the learner, it updates the network parameters\n",
+ " by iteratively applying the `_update_step` function for a fixed number of\n",
+ " updates. The `_update_step` function is vectorized over a batch of inputs.\n",
+ "\n",
+ " Args:\n",
+ " ----\n",
+ " learner_state (NamedTuple):\n",
+ " - params (Params): The initial model parameters.\n",
+ " - opt_states (OptStates): The initial optimizer states.\n",
+ " - key (chex.PRNGKey): The random number generator state.\n",
+ " - env_state (LogEnvState): The environment state.\n",
+ " - timesteps (TimeStep): The initial timestep in the initial trajectory.\n",
+ "\n",
+ " \"\"\"\n",
+ " batched_update_step = jax.vmap(_update_step, in_axes=(0, None), axis_name=\"batch\")\n",
+ "\n",
+ " learner_state, (episode_info, loss_info) = jax.lax.scan(\n",
+ " batched_update_step, learner_state, None, config.system.num_updates_per_eval\n",
+ " )\n",
+ " return ExperimentOutput(\n",
+ " learner_state=learner_state,\n",
+ " episode_metrics=episode_info,\n",
+ " train_metrics=loss_info,\n",
+ " )\n",
+ "\n",
+ " return learner_fn"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "4idyWUhW68oS"
+ },
+ "source": [
+ "### Learner Setup\n",
+ "The learner setup initialises components for training: the learner function, actor and critic networks and optimizers, environment, and states. It creates a function for learning, employs parallel processing over the cores for efficiency, and sets up initial states."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {
+ "id": "eWjNSGvZ7ALw"
+ },
+ "outputs": [],
+ "source": [
+ "def learner_setup(\n",
+ " env: jumanji.Environment, keys: chex.Array, config: DictConfig\n",
+ ") -> Tuple[LearnerFn[LearnerState], Actor, LearnerState]:\n",
+ " \"\"\"Initialise learner_fn, network, optimiser, environment and states.\"\"\"\n",
+ " # Get available TPU cores.\n",
+ " n_devices = len(jax.devices())\n",
+ "\n",
+ " # Get number of agents.\n",
+ " config.system.num_agents = env.num_agents\n",
+ "\n",
+ " # PRNG keys.\n",
+ " key, actor_net_key, critic_net_key = keys\n",
+ "\n",
+ " # Define network and optimiser.\n",
+ " actor_network = Actor(env.action_dim)\n",
+ " critic_network = Critic()\n",
+ "\n",
+ " actor_lr = make_learning_rate(config.system.actor_lr, config)\n",
+ " critic_lr = make_learning_rate(config.system.critic_lr, config)\n",
+ "\n",
+ " actor_optim = optax.chain(\n",
+ " optax.clip_by_global_norm(config.system.max_grad_norm),\n",
+ " optax.adam(actor_lr, eps=1e-5),\n",
+ " )\n",
+ " critic_optim = optax.chain(\n",
+ " optax.clip_by_global_norm(config.system.max_grad_norm),\n",
+ " optax.adam(critic_lr, eps=1e-5),\n",
+ " )\n",
+ "\n",
+ " # Initialise observation with obs of all agents.\n",
+ " obs = env.observation_spec().generate_value()\n",
+ " init_x = jax.tree_util.tree_map(lambda x: x[jnp.newaxis, ...], obs)\n",
+ "\n",
+ " # Initialise actor params and optimiser state.\n",
+ " actor_params = actor_network.init(actor_net_key, init_x)\n",
+ " actor_opt_state = actor_optim.init(actor_params)\n",
+ "\n",
+ " # Initialise critic params and optimiser state.\n",
+ " critic_params = critic_network.init(critic_net_key, init_x)\n",
+ " critic_opt_state = critic_optim.init(critic_params)\n",
+ "\n",
+ " # Pack params.\n",
+ " params = Params(actor_params, critic_params)\n",
+ "\n",
+ " # Pack apply and update functions.\n",
+ " apply_fns = (actor_network.apply, critic_network.apply)\n",
+ " update_fns = (actor_optim.update, critic_optim.update)\n",
+ "\n",
+ " # Get batched iterated update and replicate it to pmap it over cores.\n",
+ " learn = get_learner_fn(env, apply_fns, update_fns, config)\n",
+ " learn = jax.pmap(learn, axis_name=\"device\")\n",
+ "\n",
+ " # Initialise environment states and timesteps: across devices and batches.\n",
+ " key, *env_keys = jax.random.split(\n",
+ " key, n_devices * config.system.update_batch_size * config.arch.num_envs + 1\n",
+ " )\n",
+ " env_states, timesteps = jax.vmap(env.reset, in_axes=(0))(\n",
+ " jnp.stack(env_keys),\n",
+ " )\n",
+ " reshape_states = lambda x: x.reshape(\n",
+ " (n_devices, config.system.update_batch_size, config.arch.num_envs) + x.shape[1:]\n",
+ " )\n",
+ " # (devices, update batch size, num_envs, ...)\n",
+ " env_states = jax.tree_map(reshape_states, env_states)\n",
+ " timesteps = jax.tree_map(reshape_states, timesteps)\n",
+ "\n",
+ " # Define params to be replicated across devices and batches.\n",
+ " key, step_keys = jax.random.split(key)\n",
+ " opt_states = OptStates(actor_opt_state, critic_opt_state)\n",
+ " replicate_learner = (params, opt_states, step_keys)\n",
+ "\n",
+ " # Duplicate learner for update_batch_size.\n",
+ " broadcast = lambda x: jnp.broadcast_to(x, (config.system.update_batch_size,) + x.shape)\n",
+ " replicate_learner = jax.tree_map(broadcast, replicate_learner)\n",
+ "\n",
+ " # Duplicate learner across devices.\n",
+ " replicate_learner = flax.jax_utils.replicate(replicate_learner, devices=jax.devices())\n",
+ "\n",
+ " # Initialise learner state.\n",
+ " params, opt_states, step_keys = replicate_learner\n",
+ " init_learner_state = LearnerState(params, opt_states, step_keys, env_states, timesteps)\n",
+ "\n",
+ " return learn, actor_network, init_learner_state"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "GefUs8Yd7EJt"
+ },
+ "source": [
+ "# Rendering and logging tools"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "Vkd95_VpYJf0"
+ },
+ "source": [
+ "### Rendering\n",
+ "The `render_one_episode` function simulates and visualises one episode from rolling out a trained MAPPO model that will be passed to the function using `actors_params`."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {
+ "id": "DU7OVSm6HM6q"
+ },
+ "outputs": [],
+ "source": [
+ "def render_one_episode(config, params, max_steps=100) -> None:\n",
+ " \"\"\"Rollout episdoes of a trained MAPPO.\"\"\"\n",
+ " # Create envs\n",
+ " env = jumanji.make(config[\"env\"][\"env_name\"])\n",
+ " env = RwareWrapper(env, add_global_state=True)\n",
+ " # Add agent id to observation.\n",
+ " if config[\"system\"][\"add_agent_id\"]:\n",
+ " env = AgentIDWrapper(env=env)\n",
+ "\n",
+ " # Create actor networks (We only care about the policy during the rendering)\n",
+ " actor_network = Actor(env.action_dim)\n",
+ " apply_fn = actor_network.apply\n",
+ "\n",
+ " reset_fn = jax.jit(env.reset)\n",
+ " step_fn = jax.jit(env.step)\n",
+ " key = jax.random.PRNGKey(config.system.seed)\n",
+ " key, reset_key = jax.random.split(key)\n",
+ " state, timestep = reset_fn(reset_key)\n",
+ "\n",
+ " states = [state]\n",
+ " episode_return = 0\n",
+ " episode_length = 0\n",
+ " while not timestep.last():\n",
+ " key, action_key = jax.random.split(key)\n",
+ " pi = apply_fn(params, timestep.observation)\n",
+ "\n",
+ " if config[\"arch\"][\"evaluation_greedy\"]:\n",
+ " action = pi.mode()\n",
+ " else:\n",
+ " action = pi.sample(seed=action_key)\n",
+ " state, timestep = step_fn(state, action)\n",
+ " states.append(state)\n",
+ " episode_return += jnp.mean(timestep.reward)\n",
+ " episode_length += 1\n",
+ "\n",
+ " # Print out the results of the episode\n",
+ " print(f\"{Fore.CYAN}{Style.BRIGHT}EPISODE RETURN: {episode_return}{Style.RESET_ALL}\")\n",
+ " print(f\"{Fore.CYAN}{Style.BRIGHT}EPISODE LENGTH:{episode_length}{Style.RESET_ALL}\")\n",
+ "\n",
+ " # Limit the number of steps to record to the maximum number of steps\n",
+ " steps = min([max_steps, len(states) - 1])\n",
+ " states = states[:steps]\n",
+ "\n",
+ " # Render the episode\n",
+ " env.animate(states=states, interval=100, save_path=\"./rware.gif\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "N2pDHF1Q8Cn7"
+ },
+ "source": [
+ "### Logging:\n",
+ "The `plot_performance` function visualises the performance of the algorithm, this plot will be refreshed each time evaluation interval happens!"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {
+ "id": "OwkZqb8y8GYG"
+ },
+ "outputs": [],
+ "source": [
+ "def plot_performance(episode_metrics, ep_returns, start_time):\n",
+ " plt.figure(figsize=(8, 4))\n",
+ " clear_output(wait=True)\n",
+ "\n",
+ " # Plot the data\n",
+ " ep_returns.append(episode_metrics[\"episode_return\"].mean())\n",
+ " plt.plot(\n",
+ " np.linspace(0, (time.time() - start_time) / 60.0, len(list(ep_returns))),\n",
+ " list(ep_returns),\n",
+ " )\n",
+ " plt.xlabel(\"Run Time [Minutes]\")\n",
+ " plt.ylabel(\"Episode Return\")\n",
+ " plt.title(\"Robotic Warehouse with 4 Agents\")\n",
+ "\n",
+ " # Show the plot\n",
+ " plt.show()\n",
+ " return ep_returns"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "GLLqQgn1754J"
+ },
+ "source": [
+ "# Experiment Setup"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "CwFYuKpfZyx9"
+ },
+ "source": [
+ "The experiment setup includes: defining the hyperparameters, creating environments, setting up the learner and evaluator, and initialising some variables for plotting and logging purposes.\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "OHgQbXfY8LqF"
+ },
+ "source": [
+ "#### Config\n",
+ "\n",
+ "We start the experiment setup by defining the config dictionary that represents a set of the various hyperparameters for the experiment."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "S7LZsJW3K_xH"
+ },
+ "source": [
+ "In addition to the typical hyperparameters used in MARL algorithms, we define below a few variables relevant to our implementation:\n",
+ "\n",
+ "`num_updates`: The number of gradient updates to perform during the training.\n",
+ "\n",
+ "`num_envs`: Number of vectorised environments per device. For instance, if set to 512, it implies that 512 environments will be running in parallel at the same time on a given process.\n",
+ "\n",
+ "`num_evaluation` and `num_eval_episodes`: The `num_evaluation` parameter specifies how many evenly spaced evaluation steps will occur during training, while the `num_eval_episode` specifies how many episodes will be rolled out at each evaluation step."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "metadata": {
+ "id": "wexJ0Slr8INC"
+ },
+ "outputs": [],
+ "source": [
+ "config = {\n",
+ " \"system\": {\n",
+ " \"actor_lr\": 2.5e-4,\n",
+ " \"critic_lr\": 2.5e-4,\n",
+ " \"update_batch_size\": 2,\n",
+ " \"rollout_length\": 128,\n",
+ " \"num_updates\": 400,\n",
+ " \"ppo_epochs\": 16,\n",
+ " \"num_minibatches\": 32,\n",
+ " \"gamma\": 0.99,\n",
+ " \"gae_lambda\": 0.95,\n",
+ " \"clip_eps\": 0.2,\n",
+ " \"ent_coef\": 0.01,\n",
+ " \"vf_coef\": 0.5,\n",
+ " \"max_grad_norm\": 0.5,\n",
+ " \"add_agent_id\": True,\n",
+ " \"decay_learning_rates\": False,\n",
+ " \"seed\": 42,\n",
+ " },\n",
+ " \"arch\": {\n",
+ " \"num_envs\": 512,\n",
+ " \"num_eval_episodes\": 32,\n",
+ " \"num_evaluation\": 50,\n",
+ " \"evaluation_greedy\": False,\n",
+ " },\n",
+ " \"env\": {\n",
+ " \"env_name\": \"RobotWarehouse-v0\",\n",
+ " \"eval_metric\": \"episode_return\",\n",
+ " \"log_win_rate\": False,\n",
+ " },\n",
+ "}\n",
+ "# Convert the Python dictionary to a DictConfig\n",
+ "config: DictConfig = OmegaConf.create(config)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "sub4CAfrLHbM"
+ },
+ "source": [
+ "#### Define Training and Evaluation environments"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "dwMHRotOLmdT"
+ },
+ "source": [
+ "We use a series of wrappers to configure the training and evaluation environments, each with distinct purposes, described as follows:\n",
+ "\n",
+ "`RwareWrapper`: A wrapper for training and evaluating the environment of a robotic warehouse using the Mava system.\n",
+ "\n",
+ "`GlobalStateWrapper`: This wrapper includes a global environment state to be used by the centralised critic. It's worth noting that since robotic warehouse does not have a global state, we create one by concatenating the observations of all agents.\n",
+ "\n",
+ "`AutoResetWrapper`: This wrapper automatically resets the environment after a completed episode. Once a terminal state is attained, the state, observation, and step type are reset in readiness for subsequent interactions.\n",
+ "\n",
+ "`RecordEpisodeMetrics`: This wrapper contributes to the logging process by capturing episode returns and lengths during the episode step invocation.\n",
+ "\n",
+ "`AgentIDWrapper`: This wrapper adds one-hot agent IDs to agent observations."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {
+ "id": "lCqZohi0vKSR"
+ },
+ "outputs": [],
+ "source": [
+ "# Set up a Jumanji environment for training.\n",
+ "env = jumanji.make(config[\"env\"][\"env_name\"])\n",
+ "env = RwareWrapper(env, add_global_state=True)\n",
+ "\n",
+ "# Set up a Jumanji environment for evaluation.\n",
+ "eval_env = jumanji.make(config[\"env\"][\"env_name\"])\n",
+ "eval_env = RwareWrapper(eval_env, add_global_state=True)\n",
+ "\n",
+ "# Add agent id to observation.\n",
+ "if config[\"system\"][\"add_agent_id\"]:\n",
+ " env = AgentIDWrapper(env=env)\n",
+ " eval_env = AgentIDWrapper(env=eval_env)\n",
+ "env = AutoResetWrapper(env)\n",
+ "env = RecordEpisodeMetrics(env)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "PrFx-V-DNUkN"
+ },
+ "source": [
+ "#### The Learner and Evaluator Setup\n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "metadata": {
+ "id": "gp-FLgLSNg29"
+ },
+ "outputs": [],
+ "source": [
+ "# PRNG keys.\n",
+ "key, key_e, actor_net_key, critic_net_key = jax.random.split(\n",
+ " jax.random.PRNGKey(config.system.seed), num=4\n",
+ ")\n",
+ "\n",
+ "# Setup learner.\n",
+ "learn, actor_network, learner_state = learner_setup(\n",
+ " env, (key, actor_net_key, critic_net_key), config\n",
+ ")\n",
+ "\n",
+ "# Setup evaluator.\n",
+ "evaluator, absolute_metric_evaluator = make_eval_fns(\n",
+ " eval_env, actor_network.apply, config, use_recurrent_net=False\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "mCyGr8_bNurn"
+ },
+ "source": [
+ "#### Additional variable definitions\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "tZm3mf5uxo5m"
+ },
+ "source": [
+ "In this section of the code, the total number of timesteps for the experiment is calculated, followed by the partitioning of the training timesteps into distinct intervals determined by the value of `num_evaluation`.\n",
+ "\n",
+ "**Calculating Total Timesteps:**\n",
+ "\n",
+ "To calculate the total timesteps, the following formula is used:\n",
+ "```\n",
+ "total_timesteps = n_devices\n",
+ "* num_updates\n",
+ "* rollout_length\n",
+ "* update_batch_size\n",
+ "* num_envs\n",
+ "```\n",
+ "- `n_devices` represents the number of JAX devices available, which is essential for parallel computation.\n",
+ "- `num_updates` is the number of vectorised gradient updates to be be performed on each device.\n",
+ "- `rollout_length` is the number of timesteps in each rollout.\n",
+ "- `update_batch_size` is the batch size used for each update.\n",
+ "- `num_envs` is the number of parallel environments used for data collection.\n",
+ "\n",
+ "This computation yields the total count of timesteps that will be carried out throughout the complete training procedure. Consequently, the number of timesteps within each training interval is established as ```total_timesteps/num_evaluation```\n",
+ "\n",
+ "\n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "metadata": {
+ "id": "XeqzRKVPxP2F"
+ },
+ "outputs": [],
+ "source": [
+ "# Calculate total timesteps.\n",
+ "n_devices = len(jax.devices())\n",
+ "config[\"system\"][\"num_updates_per_eval\"] = (\n",
+ " config[\"system\"][\"num_updates\"] // config[\"arch\"][\"num_evaluation\"]\n",
+ ")\n",
+ "steps_per_rollout = (\n",
+ " n_devices\n",
+ " * config[\"system\"][\"num_updates_per_eval\"]\n",
+ " * config[\"system\"][\"rollout_length\"]\n",
+ " * config[\"system\"][\"update_batch_size\"]\n",
+ " * config[\"arch\"][\"num_envs\"]\n",
+ ")\n",
+ "\n",
+ "# Run experiment for a total number of evaluations.\n",
+ "ep_returns = []"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "OTMzsQEOa4Bv"
+ },
+ "source": [
+ "# Run Experiment"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "gPHBA2aU1jnC"
+ },
+ "source": [
+ "#### Execute the experiment"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "SOMJZaDGbx8P"
+ },
+ "source": [
+ "Now that the code has been compiled using JAX, its execution will benefit from optimised performance. We will proceed to train the MAPPO algorithm on the `small-4ag-easy` scenario from RobotWarehouse. The experiment follows a cyclic pattern, transitioning from training to evaluation and back to training.\n",
+ "\n",
+ "The training phase consists of performing 400 updates. Each update utilizes 512 parallel environments, with a rollout of 128 steps per environment and a batch of two vectorised full gradient update steps are performend. This comprehensive process results in over 50 million timesteps utilised for training."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 14,
+ "metadata": {},
+ "outputs": [
{
- "cell_type": "markdown",
- "metadata": {
- "id": "xKXHT9Rh3BnR"
- },
- "source": [
- "#### Rendering"
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ ""
]
+ },
+ "metadata": {},
+ "output_type": "display_data"
},
{
- "cell_type": "markdown",
- "metadata": {
- "id": "Y09oBv9cczeO"
- },
- "source": [
- "Now let's render one episode using the trained system\n",
- "\n",
- "> Note: Creating a complete episode animation can be time-consuming. To address this, we offer a parameter named `max_steps` for the `render_one_episode` function. This parameter determines the number of states displayed in the GIF. Please note that a full episode usually consists of 500 steps."
- ]
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\u001b[36m\u001b[1mMAPPO experiment completed\u001b[0m\n"
+ ]
+ }
+ ],
+ "source": [
+ "start_time = time.time()\n",
+ "for _ in range(config[\"arch\"][\"num_evaluation\"]):\n",
+ " # Train.\n",
+ " learner_output = learn(learner_state)\n",
+ " jax.block_until_ready(learner_output)\n",
+ "\n",
+ " # Prepare for evaluation.\n",
+ " trained_params = unreplicate_batch_dim(learner_state.params.actor_params)\n",
+ "\n",
+ " key_e, *eval_keys = jax.random.split(key_e, n_devices + 1)\n",
+ " eval_keys = jnp.stack(eval_keys)\n",
+ " eval_keys = eval_keys.reshape(n_devices, -1)\n",
+ "\n",
+ " # Evaluate.\n",
+ " evaluator_output = evaluator(trained_params, eval_keys)\n",
+ " jax.block_until_ready(evaluator_output)\n",
+ " ep_returns = plot_performance(evaluator_output.episode_metrics, ep_returns, start_time)\n",
+ "\n",
+ " # Update runner state to continue training.\n",
+ " learner_state = learner_output.learner_state\n",
+ "\n",
+ "# Return trained params to be used for rendering or testing.\n",
+ "trained_params = unreplicate_n_dims(trained_params, unreplicate_depth=1)\n",
+ "\n",
+ "print(f\"{Fore.CYAN}{Style.BRIGHT}MAPPO experiment completed{Style.RESET_ALL}\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "xKXHT9Rh3BnR"
+ },
+ "source": [
+ "#### Rendering"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "Y09oBv9cczeO"
+ },
+ "source": [
+ "Now let's render one episode using the trained system\n",
+ "\n",
+ "> Note: Creating a complete episode animation can be time-consuming. To address this, we offer a parameter named `max_steps` for the `render_one_episode` function. This parameter determines the number of states displayed in the GIF. Please note that a full episode usually consists of 500 steps."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 15,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
},
+ "id": "lMSKw2_q8YHW",
+ "outputId": "800c4d44-16c0-4aa5-cb7e-9b25bc08e483"
+ },
+ "outputs": [
{
- "cell_type": "code",
- "execution_count": 15,
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/"
- },
- "id": "lMSKw2_q8YHW",
- "outputId": "800c4d44-16c0-4aa5-cb7e-9b25bc08e483"
- },
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "MovieWriter ffmpeg unavailable; using Pillow instead.\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\u001b[36m\u001b[1mEPISODE RETURN: 26.0\u001b[0m\n",
- "\u001b[36m\u001b[1mEPISODE LENGTH:500\u001b[0m\n"
- ]
- }
- ],
- "source": [
- "render_one_episode(config, trained_params)"
- ]
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "MovieWriter ffmpeg unavailable; using Pillow instead.\n"
+ ]
},
{
- "cell_type": "code",
- "execution_count": 16,
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/",
- "height": 572
- },
- "id": "Xq_HhYIrWOWK",
- "outputId": "370788bd-abad-4a14-a0b4-a6b2198a4f80"
- },
- "outputs": [
- {
- "data": {
- "image/gif": 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54lWpemXnApanOmUO5ZXnQ2ZkY1lMV43TNdP0Di83y9nOevazoP2eL6MZrVJF81uSdV9q6bdaVLbWleo67TPN1QAE/9j2trjNrW4vOxJ4yWtgToXrXcXkzXcppAF0GogC8ma8sOrTryktFAKSO4DlCo6v7xxuYXk03eRa93XYxad2oVtQ6VL3u7hrLiTXG8eYmte7zO2dc29KXrW+l07oTVp4EzretPLzvmfK79T2G9HDgpW98wQwkQTMNQJvtL5oBWx0r3fe+D5tu2/96eW00138WnhpEP6ohMtLYfhe16rDw7CBc1piD59Yve3NKoLd2+IAf7hqKnYr3Tps4xfLd8YyjnFgFSwiBr8NxQHNMYZ3XGEfXzjESF2xgmq84BvvDcoHFvKEUdVk8CLZrkrGMou5bGIvw/il813o7sjsYjP/WP/L9PVvXqlc5AMt1sFgcSxLIKtayh7Tz7DMrHwQ4KlCeyq0iE60ohfN6M2O1pmldZVsCUAuWVq6snpeCTMnDU3ZChopvrUmcDNs2P5yp7j0Ugiq92Vl3ck5yjpO7Erx3NI0R9jWcyarSAkrZg33OqiynimtazpiNb/aPcEe6rB7+uvg+nqshivrsosa5mOPOdq7nrazS11tqWJ7p9om9bbHLe67zZWuX+6rtZ9d7P+GFNzpzm63cY3Yd+813uKdN5CHbO+HhlvK3G62uefK63UH3OASg/eZK0rvLCe437PGN3/1DWf7QlzYEi+wqRt+bcxJO+MPRni540qm2+kX5CX/FTjAE37vhQcZzfve8rfL4oAT1xwuDnjtMnX+WJ7v2eeaBlHOKzX0Rhv96EhPutJtlWmP8Nm1gJ5s0zvydKhP3ZRAd3rWqX6Arnv962APu6hUHfaym53cKx/5tszOdq9jZO1tZztvRRLquesE7Rvv0Kr/pWq7O8TvIft33ivubl0r/M0wJ7yI6SNYtxW83YuPOYkN33LEM1zyxva2x7ON8lpj/tafz/XM/d15YnOc3acH9sWVXXpmizztjd/a41OP9w1f7eMu/yvtyx17qc0+9LBecrKZKniKJ/7hlCd97p/7euLN7ty/V3zwBd57pEX/+JeXPrJXT/zWU1vlseZ+/1eLD37hi5+t5G+++ZMf8eXHGfLTh/bmD/9k9VN/+C+5Oe70T/OtYz3qfXZ1G1F1sEV0Qrd0CJiACriAmyWAGkGAPQeArCWBVjdLl/ZnDpgRBtAAHNiBHviBIIh62idcxudDcBd3Zfd2KMh2jFITDeBstVd+M7N3oNZ3A9OCIzJ42PdyNMZ+DkJdOKGD2beD/OaD+AGEMDhyQrhV+AcaSJhhMWh/8hc1B4KDRZKEaQd7TTgTT7iEugd822eEQ5JcQViCPHiGRTh64NGFZviFIzhlYvgSVjgaWOiFzKd5VPgnbCiDUoiHtwcXc1gnUKiEbShz81cWgZgVdViI78d4W/+IKXvYh7tHchHVNInoHIvIh/AXhmr4g2SYiZIIhh2Xh4AYiZtIgproiOfHhZ84iFkYfnHohK1oh40oinDYiUc4i4yYebtXfYtiaMCYKTHgfw9IjMVIgQWYgQAAgRG4M0PnjAwYjdI4jdQ4LMrIjEGHjDunjRF4jcaogd+4jOF4giv4dWOHL+WYgoSYihlEjulYTcv4jmAHeDZSTfQYANm0i6DXTfcITgPTalcVim+oerE4WOl3iiI4kCynfJaHhnfYi48oewc5ibCIi93nfrxoiwfnh2pDcBOpkVHIkZLjkd4XkhRpex2Jew3phkT4kB3iiytzfUM4kw45eRY5fiX/uY4CiXw3iX45+Yrr15MG+ZO0mJEZA5MgI5M1aZQtaZOHyJD1h5AbCZGr6HgfqZA6KZKhQ5IYuY9NyZQ9KJRWSZT6GHlHGZG+d5Vf6ZU0mYZP2X4r6ZJYSYnJsSD8N2ACcJdRMXTeyI0/55fZ2JfQaIDVWJiGeZiG2ZcZiI2BaYGY5pgYOCryaI5zNJlfZ5IgqXaW2XUquJn9aI+jBpQCR4My4Y8GA5Apxo5YiZQTo5QseXkfgAGyOZsY0AJmyZNviXFdeZsMxwKnwQG8yYRVKZFkmYocsBsfEH9aaTvQp5ZteYcfEB3A6XBhmZust5vKGUTHGR0rQJ3CWZBjiZ3e/ylWK3AiPAECqDiFf0h/IGZ/IGCePEEC6bmcxcOVcVmLHYQCJdAB/UEheIGeU/mSaGl9zrmUa1QAtPIfXTED86mK4Emc4tmgK6SfHcCf5uGfPgGgAXqWw5mWxdmH27kbNDCehkiK7IljmhiduzGdEsqhD+qhEZqQaEYCyJmdAtqhBPqhUhkbLXAa8mmjLiqWEHqfYCljLJABSJqkGaChLYqbJlp5UXmS94ejP+OacrmWwemWTwqV7bmjWemgQioSenlkY4oUfAmZyYSmkzWYg4mYbvqmcGp0inmBaUqnUqemASiZm3mOm8mZX5qZ9+OO6diZlvmZv2UwmDmXxNWPNv94mk7WpVIqcg1CHhrwfPYZpYBakQ0RARHAnx3AqRGQJAZyqZCaqUHJENCRAOXREzJQl6SKojv5nE5ZAT2xqj2RIhnSnDoaqV6qHFzBE7baE+5SM48Kq16qhQ1xF7WqEJAxqiqJqYoalI6xrAKAGrn6qlcWqwaaa6bhE8HaEzniNJwXoxuKpUCKfNNKrY+xHsTqZtBqriSKn8inrD1BAf6BF5FhKbpKrokKr9f2qzyxARjaE+shriearcdakbR6IgFyrc9aqtE6pQ2RqruBHftKpGy5redanQEQHbiqr9jqagl7qgHQrbzhqg9rrLxKlRbxAC77si5bsA47rhibpVf/KqsW96I5yq9/upoDWqUF+poaG685G6YWUaaUk5eYcaZ2mqdNO4FseiBFF6dUW7VWm0tz+phPW4Fbm4x4Sj9S1q84i0SCWo5iK0agOLIzyKg9K0dpu7I3OhKUKm+/FrZtO7ai1xATMAHlsbcTEAIhJ5V2K5pTKAEMwADlcbiHG7ipN7hFWXgNobiJq7gwkHIG57hlWW96K7kKobiHy39n+0Rva6prFgAj4LmTq7gT4HkDibmqyZMqgLqd67kMALiuJ7gCFbo3+52GK7sCQLsMkAKmp5Guq61FOAG0m7qeOwK327i5e7dDO2an67kiIAAiALyr27zE+7yE623Iq7gm/yACJgC8+qe7WOSKj5u3wAu8L8C6/mpAoxux8te767u47ou3HhS/71uuHJsC9csAEmC5uOtT5lukWhoC9asCjLu9BAy9QqulARACEjDBFCwBelnALqS/+Eu0kKuyDNxrGGyzTomwztvA3QumHjyQWRuZXbuNgim14xjDVzvDNFzD27PCdVpZBbDDPNzDPvzDO6wQQDzEQ1wqX8u19sO9YkLETNzDQtzETQwS6Zu/6EspUBzFAnDFTCzFmWtBGnwvWkzETxzGP8zFr6tGXyxFZAzEY7zGPGzGxutJVTwzbuzDbVzHcKy2lcS/Dzw2dezEWfzHQWwUU+zFc7wkgjzIiaeMoITcxQ6Uxn6cyHfsxnkMt3wEyXW0yJO8xpVMunuMyVykyYEsyJ0sv5UEymshyqJMAIX8yIfMHaosyY18xmj7yvQRy6Q8y3F8vm6Fy39cyvu7bcULy7K8yq0Mv7bcIb6Mx7qsx6eczBmzzJTczJbsttBsN9LMydTsyc/cy8Usy6zsyMjszblszOLsP6jcR9lMxsC8wU2KSkfswuuzzmHczvLcwo8VEAAh+QQACgAAACyaALMAqAAJAYX+/v5HPIoAAAAAgIDQzdw7Ozvp6Onb2efI2dv+iwAAenpKQIn+AADR5eVDNn6OW1SGJ1rJiRoNgHgsgWi4FzQsGAARFBCuaDv/wHX/0JerXQD/lBNpS3GOhjfJdCf/yYn/6M0wKFxOg1d3LGYsAABbRXtlMnRvhEeiZEX/oS8AVVUoKCirAAC4iCKqqqrbCxrPDyT/tFn/1qb/4b2fH0iiHka70tLcfRn/rEj/vm8AAAAAAAAAAAAAAAAAAAAAAAAI/wAHCBQoYYLBgxMkDByIQICAABAjSpxIMYBDABgzatzIEaNDAhVDVvwosmTEix1TdiRpMuRCCQliyowZQeHChg9bikSpsqdHASB1hmQplCJPnyqJFoW4sMPMpycWDsC51KgApD6VVrUIdKvEo1g5ai06cMLTszYFUvUKEWxYjWOXxi3q9u3PoFsHtjj7tMNNh2zbXrUrtmvguULr2kVsUqAIvmcnMAR8eDBhuIbZMm6p+O1mkQMkRID8tMXknJotX74bmCter53DfnZ5gvRZEWopp16N+fXW2TtVrwZOUYUFh8gdVtCANjdq2MIvEx+ZGTrvjNMlHk+evELf061jY//NPpH81+iEzQcwzl0585lp1+6+DkC9YN9VxSNVP7C2bdzgVUZffdX9VmB+6C12oFADiUZaTX89Z+CA9rkWXoKeLajTQo9BJlmEF1KoYWIj6qRfViWWJFUHEbToYgR+SSWfdfRVaCOGsqUImlQ89jiVbjRedyN+cuE4no4u+aikcyHWiGRwRNJl5H5PUrTkkjNO6GSUJHJp4pQoeqnilT5miaCIYpp0I5psKeDmm3DGGaeZRRpwnQEOIbBAZXpWZidveArQp4HtFWrooYgmquiijDbq6KOQRirppJRWaumllhKg6aacdsrpAYEi4Omoo+L052qBGnAAqaxuauqqrbL/auqdDqkaK6leLXCAQ3vmemJPDh3Q61YLBDtsVcWCCawAwrZWFQFAailkldSlWdKvSVHrbADQSnjmlhdaGxyb2xbVbZPTijuUtuUpm6265Z4rILiVwWsVueXqJO986YaLLm8VbrVvkACze5697eKbr0kDS1swwgf/O5zBgTX8bb/1SiwdxWxZXKTCBkJ8krspBfxstBc/7O+8GC/MMMof05uayIKB7HJFHktpc5E0c7XzzRLlTOLPXWqcHsdeCf0l0V/2jG3JSAsMs84yQ+c0yStFfbK3MbdstdEK9iw111R7HTLYGYq9Ndo5Xn0100ArzRnWhbnN9pFqLyW3mnRv/zTk3VTmLdQCdEp56mWhHovgoNAdTlji0GEq+eSUV2755ZhnrjnlgNbauQCO25UqraCTHvpbow94tJMits7666orCHvBrk9be+w5zj7x7bvrjjuKvq8evOzD/5518bkjfyTvxmerPJXMC29789BPbz3tzwNPvfbYX9+79xtvz/333ZdP/vni+903duuzHz3x4Kfvfvzwmx9+9s0/Xdj7GfKfvPz7o1//8Oc8Av5Of+rz3/IMeDwABtB+0oNg/dDnQARiRoHVE+D/HDg/CQ5QgwsE4fYs2EEPbpCCE+QgCX+CwfGh8IMcZKEIXXi/GS4rhhe0YQF1CLUWGi91iPPc5/9OFxYgBrF0QyzY5pbIxCY68YlQjCKkbjUqUOWJiqUSIqq0eEQiYsWIj6sVrLC4qVztSgCKK0qyfLhDEx6pWUALyd6u1T4Z8rCBZotj0KY2NAY+0I2B06Mc+bg0PybQkL0RJM4IOTc29hCR7BOcUOY4LkjaEZBhUiRFKLkuR+IRk8uSpL4YyTdP/vGFbdPkRDhJHVMe8o51U+UeydZHWL4SlO+SZURYeS9LEsiVidQlt0hJR2CWEJV4E+YwaVlIW+bQmZFUJi8T5ssVXnJiomzJNM9jzGvWUGXC3ObIuvnLampNb8SsJDS9GUFsSjOdnTQnOU2GTmY2Up7mzObL7Fn/Snyuk0D6LIk4a+ZPXEItoCEhHDyp40WkgFF0XAwjErcoqDR+SYoYzahGN8rRjnKHjJ8KFUg5NaskmpSiDfVJqsYIUjPyii1rLCgyqQRHWQ7UZzL9pjvDudBe/tOa5cyjIm8K1KLWMajg1CVRj2rUeZ7TXD2l5k+ZSlWEDpKfxcxpOzdm1UViVZ0G/eRMM8nTr8Zzqk7t6iajyk2tpvBoal0lW8fpVhjuVKlzJSha8/lOs7ayrifkal8BR8OtGjZsg2VZWE+pU8GWlbA3BGwyH6vYsUZ2qnGdJWTbaFnOOhavfvXpYm/Z2YMmll+jfWZqo0lZ1JZWrI2FqzAVGtp2/6W0Jw9FXUQhOtEjMq5IHg2ucIdL3OIuaqSasqKgkKupkqLUdNAdIkvJ6FI0wrSpkqWpRfW4VKem9bQEey1jD5s28DpMvKSNLWJbG1712tW9qWTveeEbwtWyRr4pQ69q9cta0G72kXsVqiC7m93Crte/laVvBu0LUPPmV8EGfq9s8ds1BmNXwNzNK04DbOGnTlLDF+4w3G5GYA7z974Idi2EL9vhzO4SxFT1btVUWWIR85XCZbMxZh1c4RMj1cf0hKoAHMAWB+xWt72V6G1Vktsiqse4UI6ylKf8xJMqObrPtfKVV3OALnv5y2D+sp2CFeYymznEQI6xqszM5gNUd/+7LYmpiVecrZrSGMYy3jGOa6njFvM4x2n+7p6b2ec0uxgiNQ70jVPcXvIG1tGTZfR8Ib1gQ/+Zz4rWs6QfTOkIP/rANsVzgVl811DXtq1z7nQoL03oTPt50PdM9VtBfedT01XWEqa1JhNNZwC/etM97jVsVZ1LWPcT158ur7Gziuz6lrrW/x32rHOtbNAS2StGTjJvl5ySJn/xyE6mGJXHTe5ym9tRWkYytzvibYeC+9vaVjdvGkDvetv73vZmJ7WTzW9nh2/NbS4zmXzUAGuiWdjjVZCdyTJwHuFk1J4ltmm90nCHGzzGEJ94XioeoYxLe9+RrgrHO97sSiNcfSL/G/lpPJ5wkAeS4ir/UR0PLvGPx3fjKn94yT3t7wnjfOQ6L/TJ9/tZkcc86K5OetGXEnOZszy9NY8lzHN+8Tz/2uhUnznGd77qqQO96k8nus+x/nWtW93SXuc40od+TLbru9pkVzvYuR5xXTPo6HMXetRRzpamr33vYp92v1/+c7mbPextH/tS5MR4xtvg3SqFfOTjHe51c6TdkxfZuTfP+c5TOd2Vx3IXRb/lywTczGNm1unZTHPB91x2AF+9l9983a3rXeFwjpuo6e7rZ+9697d3OVmBDWi3/9j4Qf6wrfUa/MEP39TRbrnzSb303y9/w81/PdyhnWDAJ971hId+//fBz3OTK9760Yc6+al//qECX+neR7H4VRz/49c/+aO8fuuFz367D/j9yGd7vud++ieAAThiLsNr92eA7fd/BXh2AXhoy5R+gcd/dbd96Dd+Fth7EchqsZZ95ud/GTZkRSZ5uGWCTIaC3aaC7PZknveCMBiDGAV68GZ5G4F5J0h5NUh6byF7YJZ6Plhm+zd9F5g7seeDtOcrDLh+RfhGuUdiALiAglYRISAAIZCAUciEHIgRH4ABXviFGDAD0hdyEYECF3AcFnABasgB26KAWjhsMgAZGzCG4QcRDxATyCETN9CGWbiBw7YBpPEB6rd9CyATeSgTD+AsbuiHjPUBtv8xhxV4cxBxAYboEE/xhPtEgd/3XoBoGzEQiSHHATNxiDJxAa2xiETYezFgGzIBApsoiR4wipb4FGzYMX2Yip8EAqwoEynwipF2h7IoAGfhARVzi9qXPC6wAtvRHe/xFK5ofwcWi8HIFyVgiw+ITwVwKN7xFDjgi4QnijNRAQKwjTNhitaoiW+HjMq4jO5xFs8IjXAnjTGhActxFtV4jhqIi4zViaSRA6AYfiWwiyhwisYYgu/liKQBid74fAGAAqyoiAVZfoWVAoE4iJIIEbaRiAR5jak2A5DRixZJhgEAjMPIhxx5ezKQASq5khnwjgvZdRPxADI5kzJZixuJjvDdqI8hWYcZSH9vSIcSCZPz12g/uZNBWWzWVoI66G5LOXk2qBE4mILiJoNUWZVWyTlZNno0uINbyZRPiRFB6GVAGJZfNoTHeJbQc4Syl4TEYpYGqZOYsXAEiJNueZQaN5STVpT/aJdZI4GoiJZ8CZRCyX0+yYhG2X8YOJf5CJiICZf9hZecppcv2YQ8qZiF6ZjpyJhICZnBJoWLxpnF55maBpqYdoCfSZhEaZh72ZgiaZmpiZk5qZl3iZp5qZqTuYUiGEd/+ZayaXOt6YB0uYS2KX+0GZnDGZu8WZk6ERAAIfkEAAoAAAAsmACzAMUAJgGF/v7+RzyKAAAAAICA0M3cOzs76ejp29nnyNnbAHp6/gAA/osASkCJ0eXlQjZ+jltUjSVVyRoaLGhoq2c+/8B1/9CXKxgAshg5DhUSLAAA/+jNZTJzaUtxjjc3yXQnMylaTldXqwAAql0A/5QTeStkb0dHomRFwxMs/6M1/8qK/9uyAFVVKCgoph1CuCIiqqqq/50l/7hjXEV7u9LS1Q0f0w4h3H0Z/7BR/+G9AAAAAAAAAAAAAAAAAAAAAAAAAAAACP8AEwwYSDCBhIMIJQgkOBCBAAEBIkqcSLFigIcAMmrcyLFjxocELIq0CHKkSYkYPar0WPKkyZYuRS4sqKCmzZoRZjZ8GHNkypVAPwoI2ZPk0KJGgyqFiRTl0aYTdQ7ocLNqCYYDHEKE6lRpUKZQwTb96ZXlU64Xz3LVKaGqW51a0UYkW5ajWKR3i9KtqzFvT78xdbpwW7UDw7hy9/IFANhl45OK+T5+qRbqTBCE3UogiBht5LqTfVYeK2Bxx9AiUVtcmCBC5qouOPNMXNr0RtUVcVP8XFb3RN8SF5Z47RbEzq2ea9sWSjR5c668vQKfOxqpwBUYHmp/mCHE2+Ny0y7/71tdb/m/ypdPT/vc8oDs27dnKCwbOfT0ttfrx296f/um18EXX3dVzdTZfeMxF95+CTJ2Xkzr6TQcccbVtyB/i/l3YYMayqVTa6/ldNhsyXH4oGMnQoahZClS9p91WGGW2WYj2hfWiqC1KNqL5pnII3o/BobVVBEUaWQEhmF14I0+Xhgkik3S9uRJUg1ppYW0Renchgl2iFaVV1q5JGla3jclZWXeeOZIYIapJIkIdqljanMmJeeadOK5mpt8jokXjr3VmZuguwEqHaG/IRpcAow26uijkPppngHjGfAQAgzQhiltlC5nqQCbJhfqffGVauqpqKaq6qqsturqq7DG/yrrrLTWauutuOaq660IEODrr8AGG+wBn/Yq7LHCatWpbZ8acACy0P6q7LPRQqtspQ85W6212VK7bbKZcsXAAQ+FCxUD0S0lwAHmNoXuuu0i9a6J7KL1br3ihkcAnEze6SSX43kZHlf72kimv1ICrJ6i1Ok5MEUFK5wfw+xJ3B/F6+nL78EBY+xxmqQ5/LBEEWeJ8JYmdyxyoiuPXHKJJ5tpcYYet/zwy3Gq/G/KC9tc8chQ4dyvzgnzPLHPGcslNMc97wwz0SgDjdTSf4L8J9KGqou1zxobXHXMas7M4tZSN0V1j2CHLHaOZJdd1NnoWW0e1nID6fbbG38NtcxGX//c9t0uwQ1h1l/VvHagfwNukuBQpn314YcmrrhIjKtY9+B0Oz735CdVjqbmdvdNs+ScQ5w32nuHLfrYTpdekeeiXY5i5qmr7XpFDEiK3rKmFRvvn6PeyPtivmv6u3m7Jq/88sw37/zz0EcvffLD89Wsp9liK0D1dV2P/fbac1+W98yaKKf556ffMfoNru/++wuzH7/67U9Mv/33Xyw//vXz7///+ssfzfbXvxwJkEUEHOABDVjAAM4PfgBUIAQd2EAGPvCCEUTgAgNVQQlm0IIYpOAHOdhBEIrwhB4c4aESWEKVpAsoL4Qh4WTIQhOisIVm2eAKdaiuGpIQh4XjYRD/hUhDIq4khi1EYg6N6MIZHtGJTfQhEIXCxCVOMIU3tOEU7QLF03TRi1LsYRXBuMUnjpGLYRziFTVYRiuqUIxr1CIbQ9hGKsbxh3Sc4xvVWMfbfNGPaSziHXc4yCkqkYyF5OMeBZnHNh4SjWcEZCTJE8iViM8r5CNe9r53SaVkUpPg42T4tDe9UprylKhMpSpXycr4GOtbwyoWLJO1SWbVsne3BGUng9Isb83SV9P6JbCCJy9yCeB4MZnXJO2YSBni61wPeaa7ugY5ODYSj0dr3e1Idrq4LdNBlWwi6bYJOzqF042LfOI4b1dOOzXTjN8U2DYj0s5BnROR14ycNudZ/89C3ROS7xTnPsnZzcH9U5IBNcs6XdfPRB2UkvE03Dwn0tCuRPShCiraROlZ0MYlFJ/pFKhGN1pR6mAUnBFdaOlKKp6LpnSg7Oyo5Vz6US6qlHMsfSRA82nNbI50ojn9I0J5qki/wZShMv1cTYcaUoUedaVJjR1NiSrDm04uqCfVKVON+lN+RtWcU22qF62qOKyGNYuIe+rkcvdVo+wSKJ+0Xi7lGkpbggqZgyPmn1rJ17769a+ADezzXilMYl1KmNKaa/cUOz7GYtKxnuwWYoGZrQYg4LKYzaxmN4vXk4yrXPbSKkSXOtqFSVNeD2kAn9yUAGquTo6wxSZXRbXaMP+1VmlttedZsTi6CyGgtle6LVrMSlpmUlWdvgWulYRLsNz6c7d6nO19fqtcrDA3aM51KHRjq09NVde6rn3acaP4Uu9+tyDhzdl40YnW7tL2vAO5rtmya9HiotS++6EufOU7NfqadLuy7a1595veoa0XpO3VWnLhOwD+4s1rqDvwTsVq0wUTGLcQ9iZ+hVpan773wsP1b0s3nFWP6fe8Du4JcSW8Vd6ybsAoLjDTKNzi6Ar4wzHGcDWL6mLuKhjG301xTFZM4w4nuHBYO3GQZaw3Fhu5x2kFcnWFHDgRi9a4RcaydG+k5CkzOcJZvq+TM4rjJdtLd4N7qyUhy0s2w9X/zWu+q6YgRec6J0CweM6znvfMZ74S9peGBdVkCXAtURrarmpWSS8HHczJ6rUnnz1maDmsZSgT0rSdNcm9Mj0STi/OypQWc5iv7CCyAo7IR2bkqCW60QCg2tI9TTVyu0rQDBsUwJf2MN9a/Wob+9rHSFbrVUFd4hKb+m69BraqZS3SqJGU2LiO9Y13/Wxbe3TMoma2U2kdU2vPlMTldTZQoQ3uDR/bbckO8K/VzTZhl5Xc2Ca1vFld7R0vG9Y8frG4veptpcY71PM+d9nSnWttI3jaquM1vFdtbHefeuEGn3DE/ShwqRFc2usuOMJthxYHYNjjXHEAnBU9co/EdbF1/8Ulw0S+8j67/OUwj7nMWZVok5ecIydvbMp1OcpDq7zmHXHWAYZO9KIb3eiUiubRl870eRf7fEJnutSHjpGoT53pnrZIpLOOO6dHu3CnLcqm7fVlDf+74dxGar+lWu5/VxxoF8+3suFZu8e12tUQx/e9M/7jfdfa3nQ/O+gwd3e8rx2sbV/1212Wd77Lnd1R9nu3AU/exG+Z4/V+LeQ17viqOhzZjZ975d3+eXSHfvMYF/22Ja92yrNX74HXdcIzL16Gh5va4z68OwVv7tIP/PScV/3B9Y17frt++MKX+MbtrnDd69bysG928f+u+eCj/vHunf7kq5/66++93WmPCf/ImyuA8TeF5UDH+c3Vv3O6pn8jn7IZy+U/8/rb//745+v7NZLzyLYf5fuXEf3nfwEIAAPYZv83Pg2wgAzYgA74gJWWfDUmgU+GQFZ3dUdXdRgodW1SWw1wZV4HfSwSdpAWTVxHEQzQgaulFV/3fd43a6wnJAzGgiJIgREIfjHoEirYJyAIcD6IdjlIJQyWFT34dL0Xfjo4hDTIe0woe5jnHjNYhC0Ye5fHfGuhhFJYgy8ofbMHhfC1hLanhdnXhQCChZQWgk1YhZvjIWY4haOneL5HETvoJmA4cRVog9m2fGv4JW0oht1nfWP4hGUYhWf4g264etrXE3MYJnUYfa//13lUqIehw4eEeIjIt4WISIYwUol+iH1/2HeJKINfmIVp6IhjFYdR0YelCIlvqIaTeIWcuIp4GHCouCh2dosJMAPrx3+7yIsJ+Fi/SIDaQ38tl3/GeIzImIy2UoAHGGfM2IsCCI0GKI3N6IyVsoFMl3Trgo1Lh4ZhGD8XyI3GZIDiaHQnOBFbN2lG+G8kmEwmSHY6xn2eOI8uGIhWmHvHp3ys+Ig4GIo4BXyfSI+RSHyauH21Z4c3iImniITD5nzP1Yn1CIoF2XryGJEC2YqSSHh3F3cW2ZED2Y8TCVUOqV0Q+ZGR548NmY8TqJD6CJKCSH0HaYotyZIUV4s3A5AX/8mPNEkei3eTI1lfsriTZIaS7/aT/1WSGEmQL2mQ6vWNcMiQRamSdyiUeaiU92h8FWmSgBiQngeVD2eUIxaUWxlsXgl6YOmNCFmVLnmVMNmUaYmWGTk74WF+81V+HUeNeBmMCPiMxUiMyviXgBmYf8mXAViNJKeXb4aY1uhzPOcp5Vh02viYRQeX+3iJKxSO3KiBktmOLpGO4kKZs+iDnOlZ75gv8RiTlTmTY9mVQRiVWZmUKUABsjmbFKAClsmVMDgRJjABvNmbE8ABg8I1p+mWvKUCC3CcyHmcI3CbObmQE/EAyZmcNhCchceRkQgD0ZmcFKCauMmFDJCdyfkAhf8inCF2loa4ESkAnsmpASu5mrkZABOgnsjZLklTnlKpZSMgn8d5A+3ZnYjIAfp5nBPAMtWJk6oWAwF6nDgwle4pfR6QoAsAnA1ToOYpPxoAoQuAAgzqn84JnRDqAU5BnuR3nyj1AiwgINthASKQneypllQJEw+anCJgAaZiAR8woRtpoPBUAKliAdGpoQnZoKsHoMlJo6iCo81HoilhohiAog+hotHZoi4qpM4ZADF6nDN6Kjf6M0n6muyVn/oZA/3ZnBUmETKQoCZAoDlaoflTAfq5nGPqkVxoAgE6nhSqpBiCAvKZAtxJpjVJEfopnmrapaipRRcKnjDAnHKaiQH/4KHZCaJ2uqZ4qhIqUAGWeqkVIKUb6qc8aREP8Kmg+qkSOqi0R5ypGadaKZFLSZGF+qLn2TRlaXpsipQ6aY97GHIfd5eKeZiE2XN2RYx+KZjCOqzE2me9imi++nPJ2pjI6pibGZmbOY6WiEaYiY2aKZnX+pjnKBGeCU2vumqjqWml6a3h2mlld2u0WqUU8QECsKV5YpMDY51JGZov0gIXoB0XkK8kQJ2x+nuzKpaqCgE1oR03wa+t+ZWTensRYRMEaxMQEKkHa5YJe4QScQEM+xBVYX7yhJWtSqV9ypoBsAE30bA2cQGkGpL/+K9OKV0nMLIYWxUbEKLweq7XtrI3/0YCVUGyNkEDMtuvFqej8+qqzdGyLisAhBGzXEqUCOulipqqZBmyVZEBAjAfVXECPRuxsjqxpFexNxECBJKxV6u0Esu0H+u0IBsAxFETLXCyqyqSWvuUEiGwxAGxYpu1ZIuqQRuIxPGwbMuWTGlgNquUOEsYNWCwdeuvb/uW1QEBjNu4jIu0fXur+Hi3m7qo6tq2cimplBukHFqmWGu4k9uxnYu3jOq3rwgVdNlfdhlyeXmsvyoX8we708FyxVq7tnu7q+S6zKqsjOl+y+q7noJRoOmq69hHnGu5ZZu3o1u5ZptEhVi8intSwvut0TutBUSKgRua0rtMw+uxpNu0ypELRNhbvemavB00vjL5veYLvrVqSM9rvcwbvsh7ve9bvup7v/F7vvULsJx6vPPbP+h7qvnLvgS8vg0UwNoLv/7rvAo8pcu7wPLLwPY7wAaMvxCsvw3cvQ/swOK7v9lLvN80vdCbvhRswe2DwCA8wRdcQSjsvSX8witcwQDsweTLv/8rgL+rc7q7uzwMvM3KLAEBACH5BAAKAAAALH8AsADeABABhf7+/kc8igAAAACAgNDN3Ds7O+no6dvZ58jZ2wB6ev4AAP6LAEpAidHl5UM2fpEjUpBcU8kaGi9nZ6tnPSsYAq0aPP/Adf/QlywAAGYyc/+VFv/bsf/ozggUEjIpWow4OKsAAKpdAMh0KFxFelNVVXgsZW5HR2dKcsMTLP+jNP/KjABVVXtSY6QdQ6UsLKdmQqqqqtV5Hv+8bSgoKJ1hSrvS0tcNHtIOIuJ/Ff+dJf+rRv+0Wv/gvAAAAAAAAAAAAAj/ABEIHEiwoMEGBgQIMACgocOHECMmFICAgMWLGDNmRKCQYcSPHycaOKCxZEmOC0maXGkRpUeQMBuKVMnSpEuaNTUiSDCgp8+fQIM2UBizaEOFDAIoXcq0aVMGRI3GVHggqdOrTqEKqIq161KtUqduteoVq1auZc3yDMqWLcqwMBWm9RoVbkSFBOZ2xasXa127D/n2dSp4MNO1PxNIWMxYAmKfbwFDlGuY6V/JAApXDqC58mXJnQ2HHvx4QAIFqFOjjlA6MuajAjYv/Qx4dF/bemnbxT2Xd9rSH1QLNwHU9WvKsnXD9V2WOV0Brx063ysgr+wAj08LF/7YOGbkm5WH/53ut/p1ztCjZzZ/nfzVxy62C3fx0zvo2O3TR3dPmH1y/cf5F56AlSEmgXzbSQCZeFKB5xmA3xEomoSDMWgUf01heFhPCUSAoHCs9WRfbfj9p9561v2X4oAnajgbhX2tRcKH25EgooVFOThhizDm1mNvEN634oNDGsbTCh0opKRCGICQ4I1BkngejmIVWeGPaVEZF5bNcekVT0kuuSQGwn2wYJS7lciiei4q1SZ6PFp5m5ddHRmmmE1ydyaPU6K5HJ3lyeljnH0KOtdjJtCoAHF7sqnmg4TmZ2iWfo4H6FVvZuchgiE2ut+jOzo6aZejPhepiueVNiOCCtanJUg6Xv956oClUjcrkakG9UEEvPYagZnFvfpRrHPeOmGt5Rl7JbLvteWsq5U2CKqsohbap7JzMutUac8GNWKa11Yrabifapvhpdt2++y3f5IboLmWoXsutj7CuxS36noaoLsR2uumvPHSC6S/2CVg8MEIJ6ywS9FN1OdLkk2EAFkPTpwfxIBJTPGEFv8n5scghyzyyCSXbPLJKKes8sost+zyyzDHLPPMNNdss8oY2yVxTiYdsDPPOnXUcEc4Ad0S0UZvJPRrMyWN0U1OX4QAjxv3xcABSMkGlqNoVXZW1Xpt/WnXhn2tNZ/XEUDsoOKimp/AlBKMtmxqT1ts27TyK6S1183/vVndepNI8JvC3gXwi3I7eh7gb+ONa+Pl8i2b35UxbqLjxwae5uCHK0W5YZavGfm4kL8r+WafDxY6pJgvq/mfnCf+6eJrAwk3qa9bGvt5qfe1eqiju3158Hn3rXjatVN6+3OcL0+d7PsibzfbxD8+vOmkT3483cl36XyguUu7u/GzSx/+hZ2j13zr2fK+/d/dm8p+vefnmP6bvev1O7XVZ156v6erTP7msr+79c91/9tb9lD3vsrFz1bzG1j9qhRAwwwwLQWkHvaEJ7oNFk975ePe9GwXwbhNcEvjA2H0RHhCWN3vhd/DVPpyBheHaQ0lDxsaRcDmo44NiIZh0Vh+/3z4oJsZ8YhITKISl8jEJjrxiVBMGRClIhIdTrEoVWTa0jCTRS5uMWJfzFgYdcajMn7KjGd0FBoDdKI1RkiNcEyjHNkYxza+sY73cWMe8bjHOdqRRHpMUyAFyUdAFvKP0hqkpRS5yEP+iZGI3BIk7TdJSjqykX6MZCIvib5KisWTkuSkJg0HymFFq5OitGQmDTlKTNLxlbC84yoJOctWmrKUpEzlJ3UZylracjKnVKUsY9lHYrLSmL/MpS9RuUxmIvORvGxl4YAZzVtWU5nPdGUyXYhLal7Tm80U5jC3ac1w9jKbmzTnOcdJTnCiU5zFZOcx5UnLdmKTntqMpz7nuf/PetozMMHc5Tvhyc+C+rOf9pwmQL+5UIZKJ6DrRGg7FfpQh8Kmm+7EZz7/uR6MVtSiHQUpRS+qniticYw1RCkVVbrShVjxpVp0aUxNGpOJRPGmOM2pTnfK0576tGY0hcnPouYzhVQkagRg2Exh6kWZNnUkSE0q0pA6NUfxMC1Xy9pmxDZQgXZ1S2QbjNm2esGyZJCE6uSmSF8IvQjRboTKW6tHH5pCBoYQfnD1nlzXWlcBNhB0D0zWXtNqyr5a8K+qC6wM50pSwhrOsIMpq1fOGlfHZlSi4qtgZBHrO8X2h7EhtSxAIdsXyXaFsnoV7UdVCxvS6sW0WEGt/Fgb2q//utC1c4HtVWQLQdqOtLYAXKBf7+rAvM7WtuVE7mM1W1rO6s+z8xqscoGJ27To1im8FaxvIarWA7aPfCvEawuTq9F0etB6dg1vccd7T8w6M7gcHK56AWvc3k63odutblmu25TsLla65UWffr3CX6b497MAdq/9BtyVAi/lwNHdLmgJlz4HKwXCAUuwQWHHXL0EFSQ23CoOL8ZUMDrVxB8OiVGvSikiTuinMI6xjGdM4xrb+GMplohRo1pUikRVqU8tsRhPPGSoUrUjDTCIkpdMNa1hTQAs9gpXA0zQg5IorFZTSAPy1ZYEWDgAGH7RhLlLXgX+BwFcZouXnUtA6GZY/8J87ROa0wyUNROXvuy97IY3ujk507nOXw7zv8Y8Zs7N+c8cCrSbxaxhK/d5iIj2iZ3nm9j6ave+q8V0a/0cadMo2tL/hXN+OR3pSbvVfAncs3mpTEFId9rU93lrnvGraeCaeUCHRjSspYTq67E6oqoWMKl1/elZZ/rX3T0vxzrtaTZjcNGDbjQ0vdtDZu8aXL3uILLLHOwFD/vP125Xtlkn6lpTONfgLnaqHT1tc78Q3XQOt6Vkve52b7u9gvt2vNXtawV79d7U1Xea5S0teve72//2NwoFzmWCo8/g2lZ4sgE+Wobny+H2gzi53V1oi6srATmGSIi9NuL/hPwhXf9EsZB1tuIhKuzlME/AjWdO85rb/OY4H9nJHTJUp/X4qFEDssqXSvSnFs1oUKNqk7f65Ch3ZcoS5za7LYXlsFHF6Wbhd8QRDuypZ1a4h71zpY3dWI6XEHfgPTUL681ne0d9uWDfrNg7C2oEl5vidO1wbp1tVmjDSdpt153ercv3yfr9t4gnM74fHd+wU5ruZLc11yd+a/TKV+3iZfuq365nxn8wvZhfr+bfy3law/fzl4/1uIFn9lHHvblzf27dI9x6dzMYK1rfON7LvnsUDX6/hT/t4RXfebdPvrC/J3DwYzt8Qsf59a9d/m6bD/jN5xv6e499m2f/5tr3nsJtVf3/2g/uddIfH+6Nl/vjZR/5xHcc+4TX/rO5z+i7lz7v8C/LzmWiEAdIz/+b4QAsZRQpV2Qrl1KdI4AJmHMM2IAO+IAQiET7BwAFyHJEhoATWIEYeIBBNIAndYE1xCMHMIIkWIImeIIMQRUnuIIs6H7V10kjwYIySIJEEYMzKINL5zVNpzUuaH9XhnVXMVZek3us933EZ3qV5z8qFHp4NnpVZnzl5235p3zy13f0F20+GIWtln6wt37b135HeGz3t2lT2GDSh13Ul4VQyGFliHtn2F9p6H1j6HtteBVEyD9yeH4BV4dOcYcGlIdauHB82BR+qEFG+H5cGH1VaHhX+Hdq/xh4X5eI2eeF8weGzkdtEpR24pd55LeGkGh+nmd5jseEY+eECaeHSHh9khh/lGiFlviCUriKwLeIwteIPQiIbCiLVNiKjPiKjxiJqDeKmyh6nfiJT2iMWxiM6keKkGeKXeeJ1heKSgh6w9iExRiN0AiKuaiMfZGB/fd/sqGA3giCHUiOLTWO/qKA6RiB7NiO7viO7TiO8lh0Qxdk9GiA90hGjtIA/NiP/viPAMl7qCiGAymQgGSDN7iCNZiQOOgo+MJlQxGGkheIlFeQ61F1cyGEZcMjD5kv7IKNyPiMIXlbyWeGn9KR6vKR2jiSUpeNsciNihggKNktKnmMIHmTK/8JjKK4jPcxk+syUrd4iGfHPO5zksxWk6dIkS3JkugHk5MYIT7pLEgpkjhpkzkpbIPIFBx5lEApkRPpkkm5jTvZhVDJlRIZlHOYeLdnhw5plrAYlky5h7pokjLplr94lVb5kmMZk2XZaVNZkUq5eGApiHPphkbpl115ibZXkoZZl4h5ll6JlhYJfkXpmJH2l0tZlXCpmYTplKzYl5eZmG9JlVhZmGx5mKEJmYpphGvZh235mKMJmINJklm5FFsJm3eZl5tZmp45i5aJaJgpmHGZioEpl73pFeMYc8p5MDXggULlnM9pjgQInSBGnSq2gNgJj9q5ndzZnTs1j/lYjuD/aY/kWY/mmTEiyJArmIJboZ4KGZnwSWgI6Z4juJD0WYI5WDY7uFWSWZzE+ScYiVVXdzZvaGBxKJSuZ5quSYvMZ4vxGZvIV5ueU6APdqBp+aCYaEKayGvjt3X+SZAfSoYKSogUemEWOpkYqmwItITVWIrXiJe7qZuduZdP2aLN+KIyKpvDiX8jqpUlCmYnGqJfuaMiepx0yYzs54w6yplLyps0+pk2mqQ4GqNUSpp6OY2px6Gc6KGzmZkwOqNYKoxaSoxcSqRDyqQR2qO2+aOC5oi4aKaUuaHY1qG6d6GIaKSNiaRfqKRemqN9moxP6pt6Wol8KpxoaqhOGqY8OabW/1imh1p8cMpWlTmorliokPqo/yl4EhoAhYhWb4qpPIqnVzGOAIhXpWoY4siB56iq04md6+idsBqrsjqrMDOe57mB4SmerPqBtoqeXHOfJMiewFqC/dmlpDSf9Gmfw6qswJqfYrWfXlOskRqgZaGRYjWgZMWmQWqsl5qoS+EBAuABRLmpnVpZEKECFpCu6moBG4CoftqUS9ECFaAkFVCvJdAfkPUCE7Cv/DoBJ7CgvFiLgcMDC1CwBluwGuCuVUqbTPEAqKEkqoGvFUQDB3uwMQCwlNqLqZYCFXuwMtCt72qcSpEaEJsaD3AufTUCHXuwEECiDDp9DhoRKrCyB8sBmf8KqkUaABVAsgohHKcap0sxATRrsBRTrqnlEBowtAWrAzf7pQwbABmgGiWbGhUQL3V1AkpbsC/goy+LhjH7EDKQtQXbrgYppJqBAlLbs8KRAYhzOjEgtgswAmvatXD4tQ3BAXC7ADkAotxacUpRAsIxtalxA22bPRCQtyIwtwHboOQCAxQAMhQQAh1LttIaiWibtgIgH2yrPpIjAhUbAo/7MRQgrpyqrXZbACJDARWbAnwbqSkStaqBAQJAJsKBAoUrPCxQsaEbMhNKtwZqtzAwA3eiJJFbsTZbtn0bqjubGiCQJz57u5/nuQYLuiFDukZ7XA2RtEq7A00bsn6rFA7/oCgK0AJWezpYm7Utq7gZK7AJtAFKm7Ddu7BpqhQOSyMoG0BCO7Q44LKLC7OBw7E0qwIgK7/wGgA0crLlK1wMoLQswL/ry7iphrcrC7/xa6WAGgCAKx82ILFgd7grm7gOHKV7Sn4bcAEmfMIXcLwV3KRXGgAP8MIw/MKbm8DpBwE2fMM2LLchzKgu6qhO+6dguqKiirEiTKhTasFI/LSBOq4sysM36sPe27o6qajfJRukCo4BaJ06Jp28qkOv+sXXoYC0OsZkXMZj3KsWuH8aqKu5uqpt3KoZmJ7DKqzDWp8piqBshKzuyaz36axWA61lU7k/rLBgBYRZga1DaLq+9/ip3lrF1OjEUgrFBDzASTy/S3ykRVypR8zCk7zClkzFfJnJGrvJQMzJlVzAQpylc7qldYqid3rJeSrK7EvKhGzKtvzJqSymq0ymrWy2d5yEubyou9yovZy8yOu6jHma/eu1i4zHpwfLyvzA/mupnlzLpYzLjqzK4kanRWinz6emvbvMddvM3pyhaNfEw9zDxRyprwzKNQrJIyzJp0zJt4zK2azL28zK3ezK3zzEO5zOT7zOOHumgyyy7gyl8GzE8lzP9HzN9kw/cprPvLzPvtzOwUyWsgzBtNzQ1tzRBn3RHqZDp0pfI90XqfrGXYzSNdUm6hjGAQEAIfkEAAoAAAAsgQAbANwApQGF/v7+RzyKAAAAAICA0M3cOzs76ejp29nnyNnbAHp6/gAA/osASkCI0eXlQjZ+j1tUrGg9lCJPL2dnaEpxyxkZyXQnLAAALBgAMChcox5FChMRZjJze1Jjjjc3W0V7qgAAq10Ap2ZBxhIq2HscU1VVbkdHsxg4AFVVci5qpSws3AsZnWFKu9LS4n8VAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA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sxGmjz7mwiqu3lRu2Uvu5dhu60Zq3/5q2hDuiE3q4FZq4TSuxewugYkuiZGuitvu2vbujpnu5U5u5dyu6tTu5Jzq3w9mc4Fm8qzu6rcu4qem4eAG5nvm7kku7pcu3uRu7JYq4i/97vNoLvNx7uswLuttJupS7vsk7o8I7skv7PUtbNCCrERnbsTBrly9npGlZNCj7vwAcwCWrsgzEsnrpsT9XvwZxvx/rl+8yLk72gSJlswGDs1+2uUV7sDfImC0xRkE7v1/ru8grt+3jwQq6OV2bwTyrwvzpKSactJoJwhjMungrvVnjbVn4wbYXuJxpIzg8FzoMuJwbosj0wzsSxJrLww3KUC+8tUq7w0OsnGthxOGBxM6brjRsvNubFFScAFasulgMvTV8u0HTxV+cvtErvuG7xWXcxH4rn0LMwoLrulPsxpj7t0kcxYNbx0hyxjs5xiI8vu0LJmZ8wi+Uwln8vGr/HJsWUsgwvKAyrMRK3DiO7MQxDMVy3MNM3MeGvEORrMeJbLRGasfDi8dXrMFirMXky8WkjLrEC8aovMhpzMadU8lvPKB5nMlLLJatzJfYKL9gmb/wq8AFwcA/16Ttw5USJcDM3MzOvIEEjEAGPLPE7GMi68srKMzYfIJ/eZd/ScFCZcFSN8OprMgI8rMW0ZgE08nxhMjlHMayDJ2miRXsfGbuHM+qnL3cqZvod8dwnMuhHMu0TJ1968+4fMorHNAJbcNqW9Cl/M8ITbQKLdEM/brU+1vWG6P6rL4bjaP8rLW3zLWfrMuT7KB+3J6AzL7YS8KWS7cPfdCwvND4bM6r/9y4Du3KphzTFD3T8DzQDd29hvu9s7vGKj3CKuq+Lo3TEK3Tc/zOAl3T03vT55u6aJzSHW3VLB28ST3Vr1zV+bzSRo21Hw2fIf3EcTzRTU3GUQ3U6Cm7blvUggzWYn2fhVLPWvegaB3CUE3QbM22bh25ck3UVy2I84yZj4zCI53X5KzWfG2+RCoADqAlkQ0WDnDNj13NA2HML7tia1PZ3uXZzxzaoj3aNabNl23aSprN+LvaH4vABPUvBxDbsj3btE3bmeIRtZ3buq3Xg03Tfeotuh3csp0RwC3cwQ3OXSbOO7jYgSzYYoLOFaHOOXjSZevcYW3dc42idX3Yh5zYTv8t083NyNrtnQYt0pis2ET8XcxZfPOH0l993Ryd1eX7vlyd017t24Ed30fd0svL3s553z0N19i9z3RN3i9t3mf93Tvt0xYt1f7dvEzN2/nt0QXezwdu1gCt4GnN4OpduG0t1G/d2wEu4pZZ2JBi1/cc3rMs4OKtvPhZ3hge0RvO4is+yFrd32Pb3tWt33EN3xQ+3hau1DAN4E9N4uC91z/t2Dn+33/83j3+5AQO5CAN45ec4Dxd5PKNu0quuzrOuz6O1V9O2P1Jz9ztyeet4Zp8tNTt5VDO4wPe4kiN41uKEJOtMXV+FZ6N2QKh2TGr5xhr2and2SAD2qRd6IZ+6PT/g9pzrtoJ7Np97uj66+d8jnKg0QCWfumYnumavuBGzumdPuO/bdzCTdyifty4BR4yCMpo3l3QfRFXFzA55VyqfuVHbuPzvdVo42A4GYcSHub4vd83/uINJuskPescXjSeKye6LhU56esjPuFgnt0uvt341Vi8ztw0Hu1RPu0GLibLPhPN3uY1Du1Ovu1xLuzeTuzobexI3uB9rUzfPpKQ1eviru1wzt/oruzqvuqSLMXIvt4hFO8SOe/Y/ulpbuIJ6l7WTvD9rsswCnoD9l7sbvC7jPBIW+25xfATT+4GqeannlnX3vCK/fBkjvF5FfIb7+wlPuaGbfIgr/HF7vAm//3xpYXyMT/yM6/wGZ/y9V7u9x7s1B7xC8/zbz7uwH7rch6yI6bMqMICgK70kv70UA/p8Cvon43oWJ/1Wh8sij71rN3oXW+/Ui/2jA723ALbpV7bt10SaZ/b9F70qlrcbT/cbCH3c99BGHv3s43c86LcXfb2bl4UrZ5liZmzgw91aw6+gW/02Y0BAoABQB/5QV7fS03ktc7xoswQEZABrpIBnr8Baz3WEE/lkHzmtN7CEeCQAsCFKhD6FT7lF17lGX76B8oQXAiQChABjf36ZE36iG36Ki7PAZABt/+QXbj7Ut77sV/6Vh78hO0A8Gj8XGgCSc77o7/8v9/82U7YIv8Q/au/hqDf4R5f5u3s3bSf5igQjtLPhSLg7qJf8mUt+zIO+JHY/d5/iOFfoBfdGBntn+e/ywCBQsHAgRYEWCBIUEQAhgIEEGAYUeJEig0fVsRYEcBGjh09fnSYUWREAiFHinT4UeXKjSlZvuToEuZLhxAjmkj4wcKHhAM3RKx5EuVFoRhnsjRZlGJJAUoryjyqEmpUj1OpxiTK0EFPrgpMSAzqdGJYsRGvVm1almRStVbPur0KlypZhhm6Jhybta3esmc7si3LVC1QAX7RGv5bGHFLvgHuDoyQ1+Zgun0RAxYreHAAuVE7H/08s3KACHdVUBztNLXSxZidaqasuLX/7Mu0DaeOkFt37p+SN3Nu7HT2b9h7FzM+DiA0zNVFmwt9fnL45uJll9O07fc60uBKo4/8LnL64Opit688LzX72+7O20N/L92wAYcOqNcf7MChgcX0BfBHzD8A5wsPI/3iE+nAyco60CEHH4QwQgknpLBCCy/EMEMNN+SwQw8/BDFEEUcksUQTT0TxwQD3649FAv9rEcYVZXxxQL8EjNHGs3SMS4ADfgQySCGH5M+hIY9E8oD0QFqvx+SWPMyAJKf8MSUpqUxyNgYGY0BJAbZUiwEoE3uyybnM9MxHMMsSU00ux1OrPNXQBI1O0exkDk/sFrSuPQwEwEC82ohz7QEI/w5FFIIHUNOTuzIfPa5AjFILAQINBNAA0QkogjMwzCZYIFRRRfUgL0hnO7U2Ps1rbIVQHRR1BE4HvS8tiUYYNVcITI20UfR8Va/MVefk0wNRYRV1UYk6zQywB3KFdlPCUr0NWCaF/a0yCI51KNc1A2D2NbYYgBbaCsCy9rBeqdUOwaEWBJVbAXINYVlayWMrhHKhVRY4dtn7t8dhvdMLV3mhLZWhcJWCLd59c512XYlR7XVg9yZ7dlRkRz1X4XvjNOlPCS8AgV+LAj4T5TQthm+yCnLdeFQwFy5KswovyFXWkyeuTeU6WT6JLA5yvUAAnHU162NP0xI5QpJz5SBiiv+n7rnibPV6WVQQnvY26WoJtZWhh0XteGeqq/X5TqDB08vhh/sFV+lmww5g6LGlNbvqs9vFdjNtx25hIpqFKi/rcutFN+08Fd/zaj7JfThqe7+ulSK3u06cZ7Q1b3ftdynK2NxZKceXboY4eCB11R/AW2q9X998Ns8zSm311ROevF2wr2bcUc7Zm31SdzMa/CQ5Ce791+TVC/6p4Y2SW1zT51z+2t8Fdvy3Ge0rnXsGXawxR/FnbH4iBTdrMEX112e/ffffhz9++een38IZeaQKx/DvH39//28E341mg6UkFclHBETSmLBSPXWh6koIFJKVIDgkLXHJS99ySpsYSKb/653pABhUSps++KboMcw1yOtgmjaIlfL5ZjPFG8nx3LNC5KTwZ9l7YQlrdsIZ2vBONFTO85zXwtyxZ3d+SxcH9wYwqyGRiF7TXeWQCEQF1lB2OBwMDEUiQ/hQMYkLbGJsnugx0oFseihcopN8mKcxAqeNWswIF4P2RSvCjm9h3MsbdUg4HnZxjdjxYhslpZE9Gq+Pc/RiILGoFjhiRI5sS+QfuSNIIY4uiqXjnSSVp0nmLdIyZVxaJtOYMk4yiZJ6BOXcRGlHJo5yZZ4USyMr8sihRNKVN3Si9goZw0NCspQNZCX2cpnDVEpvlbELJimvOMws7nKLvazlL5WYzFcyKZORzowjNGlHxyDaUlWwFM6LvLe0cTrlQPiLiv7+t6MAsrOSEjlffgICACH5BAAKAAAALH8AGwDeAJEBhf7+/kc8igAAAACAgNDN3Ds7O+no6dvZ58jZ2wB6ev4AAP6LAEpAidHl5UI2fo1aVTFmZmZKcpQiT5lgS6EeRaNkRK9pO812JCwAACwYADAoXIc7O7QkJAoTEVhDfWYxc3xTYqoAAKtdAM0XF8USKrMYOABVVU5XV3IuamRMTN0KGdt8Gh9vbz9fX19PT79wMLvS0gAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAj/AAEIHEiwoEGBAgQwCMCwocOHDxkcSLgQokWIDBIe3Lgx4YGKF0MGyCjgo0iRJDmqJJiwwYCXMGPKnJnAI8iTGFfqTIhTJAGePS9q1KkyIYGgQgUcRQpxKNGOAhDMnEq1plKmEJ9yBIq14U8BXR061VrQaNiGZs8GGEt2YEKpVOPCtLr0bFuDXMN+VbtWwN2yV9WmPcv27lu5iOny/csSrNq9gv0yRhiYcOWwhdseRhxXsdrJlPlCtgwawGDMl7tmJruZ81TPdkHn7ToadenTqlNjXa21tWuZsMPedny2du7hdVEnPy476u/XuLEOFz17t+TJ0ZlmR8r7qe/nL4N3/53+uLr264y3B1XfszvR7+DFS29Onbjt5sut5z8/HC74uezhRF5x5nGH3l8BnpSgSO7t5Nx/AOrG1IB6FbjegYZJaOB+BvYHYYQcBkUhbRa2h6FmGl4YookefjiAfBPSV559zGGXookr4tTgSvA9ByNSI2JlnHXI8bVgSDsW9eCHP4ooI4E0EomfkTfqeCJrS0LYZE9BMjUkf1NGlqOCV/aW5X9bCvhkhVGCaeOYDFZJZotMHnlRl0h92WGYlsGJZJnenRmfnRbhGZSeFxYpJpV0akkoRAZMZkCJJzGAAKVkRsrYpFHdZB0Cnp6n6V+cIpDAqaimquqqVoEaWUKwxv8q66y01mrrrbjmquuuvPbq66/ABivssMQWa+yxyCarbK2j3lUqAdBGK+200x7wLLXYUnupAM22xakBB2QrbrTbgjvuuOVKmpC552ZbbrjtYovAcKEGJRFFZ6XUnElhkcRvV/ra+C9W/tbbEwOG9oQoi3wqx2jDufl555oktrnnm1RKbFGSW8kZp8ZZUSwkpnEq2ufDGC/6mchekvynyQ5HBnPEi7Gcp8tJzawfyul5/CfIDyWM08JW6nwe0GIB+p7PSSHdkNAnET0nxDvLTPXRNdtYH88IMr2x100p7aDTDD0atM2H4vy10RtynSHZfcENtU9qh822im6jCLfZDs3/HZLUJV/dttUpn7yy1jPmjeXeYD/EMVSMy422wnU7LjaPjSfN+N04Zt3z1oT3HLniZo5++OeJh9616aQJjvfpXYPeeuExzy56xp7Hnrrtq+Ou+tu+x4Y4lKQHyvp9tNMMe4ayI3+7yrwDD71wkw9dedKcW7m5652r1S1ZnKpl6fVocatup5G5atn3WpVqsInqo7bs/PTXb//9+Oev//78998r+0+5VryqJcABkmtd52OXAQ/ILXgtEFrp2tS6GoCAClrwghjM4Loc+MB5Ned9lZqIQvL1uIPYhIQlASFKSogXAbjERVYZGFMQVr2oka9sl1PS8WrUM/84KnNPqyHd/yyWKO5pr3hL8yGa+BbE4bEJiWMLnvO6psRBAZEhfrsI4F5mRDJtz0ZV9BETsSjEv92wL9nzIhQxF8bfpOkkWbTIFnPWxY+tUUltdM0bRRJHiMxxbXX82R07lkfO7DEkfXzIH+0WyKYNEiqFTMwYA5BIhyzScmm04+9QFEm5HHJiTqzYI014xbiNsoWd7MwkK+mVM7IQMDuUEhhd9KJVllGLrsxhx2Lpph7S8pOFuqUcc5lJQW4SS6msii1DOTIiMix5VYseJ3+5TNQR75ilkyIPqUjNUrKSIZfEXiO/9kVfwrCaurumNBenTVmas07eFKYfiTnOsJWTm+eMJzNb5v/MotXTcvfMUDKho09rPhGbxmtnL/EJT7gBkCjhy9e2qPRQnbgvfSqMU0VXUipWeZRVb8non/xH0pKa9KQoTalKV5qsjaqkVBmMaUwbAFOZ2tSCCJRgAx/IQAV2MKek2iBPISjUoXrQRgmgZQMSklR4DkeGSCkYCp8qUotItV/DaeqHtqXVJeoScgq92PMMJ7yedfU/XM1nMR2J0KXxEkjNOSt40urUf2rulLAMq5OQSku6/nCt5MRrY966V7P2lalqtWv5Aqo3vXIprocVgFzd+MrBOtaf0MTa8lA02d/41auAtadg3VJKdGaos675rBVDC9DRUoawj+Wri1Qrxq//khK2mB1r7agn260itq6ZHdw6szm98UB2tr/9q2JxyFh2Fnc+vYUQbSlr2xbidmrBfV1Zu4ZazkxXj5Ul7XUDl93ubfe0ke2uJ8P72stiV7fKOy9n05vY8h6xrVF8boyii9bkgna5psQv5saLyOP6VrL1hW80pyg9svLWsMhFMHAVrNnhJlS/cOXvXP27WgBPkr2mKW1BuUvfCfcOw0W0rxq9d76PuhhVXH2xi4HqrJBiNDIu5chFLRO/3LD0x0AOspCHTOQizyrHGyngAq31lqESIIJB3alRi8pTKNeYWxS86U2p3EF65UuEVcUIiE/YLzIDbMwu7CZU7fXNAISz/3ysvauA8dhNye3zZv1874kdvE2B1jl3zNsdgxuL4me+84d2Nqgo57xL95L30F5NdDoPamG3OpqLs8ynpAOtzkE7l8/uZCiiAY2i5vWZ0KBeqJ81TWosmTrUDd7tqZH55826WtCzJm6qxSrqSLfaTK9WNaplDetpstrWwMZ1sT9NbGHT+tjyvXWnc33hXacY0lbcdKmV7WxdN5vXq27orwMVbHAPO76eNtNAaWLabU972d5GN7WTWOtoJ/vd3a72t6/d62yPe2nl5nes5Q3vQK0bOO2WNqXTrW+C55ve0H7wpBdd6fxa29D9rq22FU5xhlu60LnNOHUdej7xTRTH5//r8afCHDYkH6SjMv6ojS1j5Jrb/OY4z7nOmZVyJzM5Kk62sre4vMB3BZ3GQ5dylYluwKP2jOUOudcIy1xdwKz5YGYmGJqvjpOrAqzNb8ZhnBfr2hATGJSKbmbZPyzisTP33w4KOMYHvuB5W3zfsU07P9cu4ubGu+7Q1Tue+X52QKpYk/YmN7fNzWyHM/7vFZY4pxdu9wFfmo6HN2biAb54gZ8b8A+/u+MLO3G1MxqsINcz3SNv3DunLc+P3jPeQ7564Ure3ZQv+McvTvvPsz7wpd/76W97ecNT2PatF/zrCV98RmaerZuPe+fn7nvk6170oM+w8ikHe0wfX7sex/7/7/e7fet1H/PfN2/4LZ96PspTkfR8fmCHb93mn8313Gd++70v+9HnPfiDR395tX/o13/ZR3qT13GVp0P2J07yJ1osplMml0suZxA7hhoqJyopB3VJk4EGsnMgGIIiOIIkOD8VWBDf0nNTZj465VNFx3QDJHTgA4PxYnRGNRxeRnVUhUJcVylbx4EM4XUEg4Pv54Dpd18Vx368B0fN0WZoVnjOd4QrFn2Y44RVZ1kEaHwGOH7a1zVW6HYBloQMmIUhYyNf6GFth4YbZyZn+ICtJYBYuITuZ4ZFCGdqqH9yWGB0iH+913gHSH1+yIUIiCJtKIWIt35jmIdo54V1KHZ3/wiH4tWATdQzhbiF1hd6Sjh7TLiH5ad61Qd+C9hoZHh/lNiIaPSIYiiKihhMnAiAnheIl/h4DfeHatKKCXh9meh/fQh5sTiIWFKJtQeKuJiImjiHpciHngiLwoiJxKiLm3iMnRh7wah+oYh6q1iG0OiKgMiLyyiLu1eMepiNt8iMqgiOBTiNSBgWJ0gQKSiBKKdTHngh8Wgi6zgQF5gb82glJbiP/NiP/liCWnZTNNVkQ/VzCHB0LBhlLth0NNguMtg+DXkuNlhl6iUXSzV1AANmU7UvQDgSWTdDP8iDHckAFRkXJ4dv3ih+vbiNsyiIPSFXEBCTMhmTLDATJ5l75P9ojeaoheg4hWFxVgmgAEI5lEN5VjepgMNYjs4ojZ9IjVhxVhtAlFK5ATJxlKaXijq5lPzXk4fYFV0FAVIZll1llcKHlcQ3ikZoid0YFF3FAWEplRwQE2QZgGZZf2hph24oZ2ehVSfwlmEJATAxl8sHie11l46Yl2SnFk2VACPgl1I5Ak0lmPlHmGYniYdpiJr3ky+RAo4ZlifwEpJpfni4k1Golk7JFE0FllIZAhgQAn8JmmcUdqeImG9HmWwHN1oVlUSJAQKAAVMZmLEZf5gJfYiolLR4EloVlEQJK1KpVaFpQ+fHk02ZjtV4ltf4EF3FmUPJnEPpAnIZnNFZmlz/mZnVaZfX6RBdxZjbmRBDCZnfGZ7wB59pOZ7EWZ4DeJ4NcVYtsJ4CMJSA+Z5yt4stuZIC+o1aeRFytQEcwAGwsqBUWZXgOZoHKp3KeJpIgVqwYpIRapt9J6HHKRIYmhAaKp+tRKKXaZrUmZRZ+aEhgVodIAAdMKIBmozcaKGvWKMpippxYQIdYAIyOn0FqpJryZIGyqIIGnMe9ZwhMT4m2hf1KBD3uHLvGGX5SCZVGif/mKVauqVcilJPCgDtqJAtmEBkOqZmKqabQoS2+ItN2KZryqZvWhps6KaMGKeBoqbiSIhyaqdLQ6d1mqdwCqhzuqd/Wqh6yqcOgqeGeqiE/8qoizqognqnfpohitqokoqoSlKpjkqpk7qplpqonQqpjyqqnIqpmfqpgTqqfRqqpOqpqVqqqLqqpgoVmvqqrtqqthqrtMqqshqpvaqqvwqrunqqs0pKtYqryHqpvjqslHGswXqrygqsoFqsqIpmvPqsuZqs2KqtzBpizjqty1qF1wqu0sqs1kqtLfSt5Cqs7NqtgKGuxBqu8Vqu89qu7kpa8Nox+aqv4yqu6Gqp5yqv/PqvjbGvu3qvxtqvAyuwB8uwCUuwhBqw9LqwE9uwFfuwCPuuCmux9sqt/uqw6QoaXxqmV/akJJt0I4t0M5iQJVumUdalMBuzMjuzw5KyQP9XkErGkCyLsi7bsi3IQS+odF32QV+GLzq4sRwLrUvTgyuUQvkCdsJ5sSELsYWJn2SEjEPEdwaLsSjqk8mnjaLJoVs7tcM5f1SoJDPKlNkarR07oENai9FoRk0qsVKrsbQZhrfHcVdZnEm7ttvqtjb6f+NIl3zLtW37t0XqknALttCptUhruNPptcA3uINZl3YLspfbtV05ubiHlDkJuX67rpG7ueTHuFkrto9LtppLnpyrt2VZuKp7uKJboTnahZQ7mZZbsKmbufRptnl7bziZkh9bt7p7twkHvJ4rvPWqtLOLo5JburcbtrmLr7tbvGULgWfbMWm7lcw7vLKbi0b/yopxi0tze4XUS7Xeioq/q3goeaNs273LS7vPa7udu7f2ab3Ee77X+4ZnYbMTWL42G8A9m3RX+icFnBQ0m8AKvMAJbLMHibMEuXQ7u7ICfKYlC7Q6awBZFpAxlYMZabRnZr7NWr362zVMu6Qt8UtQW75jy7vwa5zncXAxkQAr7Ljo+4RUIsNzUcOoe8MiXJnpo8KmKJt0+73x67xxosPhwcPTO8I+DIY94kZMDLsuHLreO7oGLMRYK7c2jLn427tho8QvMsX3W8JGTLFgbDliTMNDHLVn3Lce24yfosXjO0wsTMJO7MWRGMQwRMYqCrpxjMZYnBRr7MefG7svDMeA/2slhdzGd/zEeAzEPEbHpsvFPazHeby6hEzJ0du4l5y/mZzGHcjJ9fu6ZRzKiQzIi2ylpOy6hHvK6QvJxisoPmLIyivIVnzEq5zErYy89vvHiJzLuIzEWdzHjtzFoBzL+zvKMOS/EkWBA0zB0QyR6MNjSOpRDJzN2rzN/OjAPpezMaiy1FzBaKqQGBzOQlt0HkwwGnm0sozJIXbCFyGEM2TL7ou4zfu+8ku69OvKlUvFX5zKwUzM9dm6vmzKwFzFgazIiUugz1jJ5IvMb6zKDf22D93Jp9vEypzMOGyYlHTMnzzRA73LrAu9pfzKCR3QwszQQhq4i4vRlqzRRSzQ9/9JmqQI0XYs0TSt0gTtu18L0xEd0jttxoPs0wbNvsF7z/mMz1e8zyXdzweN0oes0Pq80NZp030D0jL9wzPt1AVt0v6MuwBN1CtN0S1du74Y1f8My11t1eaJ1ZOI0/P0yPDc1iT91VCN1Mmr1E3t1lRd0S590Se91ilN1n7N03dt1GCt1mLN1lz92FBcSjbrADNC2WHhAOIcQJkNUZttUZ3NUWOE2Vck2txc2qZ92kNGzj672qzNsxbs2uXsLE91ALRd27Z927cdKR6B27zd23bN1Lrcp+DS28Rd2xox3MVN3Os8Q+0cwi2M2MDdMfJsVR8ZVfZMpEud3X2d2Ni7vpz/177Yvd3RzdLgq7iCHdbSO9aoXNYjDdhoLbiD3diFvd6Hbdjczb/eLX3gHaTird3Bfdbzm9Z6/ctTDd3+Pcz3rZf5jTnbe45DTd9VneCJmb1Q0eAUWt8QPt5XPaHYKNfxqdPs/dcAzs8C/t1JHd7/3d8qLsfmbYwebklu/OAbLdI1zeE3DdQ5LdQhbuDl7dAujuNzDeIYPuNF3d0/Hd/p7djPbd/uHeDwjd6evNVLnuEj/tQlrt8nzt8pvuUI3uQk/uSMneTzTeQ7zuRVjtdXzuBASqMRruHtfeaKndcmvtco3uUHTt4s7uMhMdmVfRairdqw3dorSzaiTeiofeiI/57o/gPo0hzbgf7oje7okS7bzdEAln7pmJ7pmk7lXI7ndl6FyJ3cvH3cok7cpF7qvJ1VSsVev33npDTdYua0VAfrEaHqERbJrd7jAaABAqABX/7eLz1ft/7OHC0hEiABsHLsEvABRh6+HY5ew17XkE01EiCU3KkAKtDsLR6OJBbtxT7lktwQ/EmUEoDfNp7VGvYc24Lr0548FDDuRGnuzn7jwn5g7A7up+EAy8meRFkCCn7ucQ3t9k7sNB6HAVAC+96fUsnstWm1H53unsXq7S7tdYECUnntQ0kCEw7XVwthAy/t+F4ZJHDx/L7wDc/xD+/x0iXxIW8yFr+bvRmWGv9/8gDf8d3+8d/O7skx8kPJmq5p8jQ/7+iu8v0lwrne6Vet752pAP6+8TWf8je/8kY/8cW+HO/emfK+7Yso8FJ/7zoPEZ1Z7v8u9AFf711P8DIeHdX+ltme9Xq+9WZf9F7vw/shARRw93hPAQw/9lovvlEv92hf5lWL8scbKCVJFese+EN+m5YJ9VwP+CD/9R49STZ7zawCA5+tY5mv+RM8zudj6KMd2oo++qRf+sfC6J4v6amv+prd+a2P+gE026ie2/E8+7h99J+O9Oka6rYvQmDa+7a93FHV3FpH9QVv5phD61FX3Wym1epN5ov/2ITF677O7UD+4Toe/ZG9HxX/UAGw0v0VEAHPjuRR/vy47+kr+nsPsAALACvsvwAXMP5QntHmb/xp36EO4QHv7/7v/wD0Pv8AEUDgwIEEBAggmFDhwoMAHD6EGFGiw4YTLU6seFEjRQEbPR4ksFAkQwEhCVpYkPKggJQtE3rceHDkzIIyac7MCBNjR50Wc/aE+BMoR5M3RYIkGKHlgpVLF1QgOFSiTaMLDSKsSlJqRKFDuwL92hNpVoVjBV5Y2tSph4Fbg2IlS/Bq3IRhddqFifcjT7cAzNINYPaBU7VLX7Tt6xcu4LmABeqNydctZI2UL/6laxZt2oNOUzIQmFixY4GNHVv2KXkr6p2iMcc1C8Jp/wYBGTxbQNyXKuPdmVVLZT31t9fhYEuSDnx84OaUIjKI8Mw2dOLecU0DDs61uNjtd7vnVX46fAClnp1OiEp9Md3rvl1/3/v+fVHs4wOgNO8yve711quTze4t+dSbDznMGMgvpQdeUg+59mCDL7IB+SuQtNcGM+8Chd5z8L+sAnwIRI4mnMw+COkb6IEJVmRxggg2bJC0BwGMsLIaL7sxtQrFQzEuDmX0sCoRFSNxtRx36pHGJLP60bEZPzxSuCKBi1K7JYU0kSwD+jLgIAeAFOBLwBw4aEu3uhTAzK3QVFMqNrl8LSsys6xqzpXuxDNPPffks08//wQ0UEEHJbRQQ/8PRTRRRRdltFFHH4W0zzaHevPMMi1NE85MMZ0UqErXvJRTLt87oFRTT0U11S0PSrVVVw8YMtYqBSTQgFdvLbUhW3F99T3QAGMAVgF+pYsBWackjlRi4zJWgAOWJYuBJnnrjyANBNBgpGMJ5Ja/K42Kk8kYnfzvAQss6ECADs5dsK5ZQ3x3xG5L/PamcKualr3qymOqs5SkG2hbCuc1sl6a7jUqX//6W6GlwnAjSOASkTXOYJzoxHdcahPCUCV/U3ox4HiJJJjKHeuzeCSFyboOQc5YaklDkSnmjmbvUj4K44Q11nexCgj7WMGZS06W6IoN1PmmlbMyjV+Hg05p6IH/p55YPZxJuhpG/jqE62egYV6qXYmNtBm8rN09e78SuR7IA89os22pFaSummqyrUY67dzWBpOgCZxyDrqlQKD7brtNxtvCpGlauqr2Eow54pHHRtzbvJFr3Kj2ZIM85McmB73svfT+nPQAMr/pQeY8g0py0SU0mjvTEVaa54UVctopaCkv+nDiZl98JtRpmhGEB45H/gHPC6+8bucLvpy04Wd6EsvXbbweR+BNn36k6sENPXbvTvaNe9tZDhL87HUU3+zoHeu0JzTFpJb+uOaMX6dP3QwV1E39J92cAhgpAhbQgAdEYAIVuEAGNtCBhcofTPZHqf5R8H/8u6AFI+iRNwl6qoIaHJV6ePWqVTlrhK7inXHW15pdnRBVunJhqnzlmGAdBFpZadYKpdS+vTyLhqy6YVUYEBAAIfkEAAoAAAAsgwAbANoApQGF/v7+RzyKAAAAAICA0M3cOzs76ejp29nnyNnbAHp6/QAA/osASkCI0eXlQTd+mmBKomNFMWZmiFhYkSRSpxxBZkpyznYkLAAALBgAMChcZTJzhzs7tCQktGs3W0V7elJjqgAAq10AzRcXABYWwxMsAFVVTldXdC1pZExMtxc1H29vP19fX09Pu9LS2Awd0A8j1Xof4X8WAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAACP8AAQgcSLAgQQECGARYyLChQ4cMDiBU+LDiQwYIDWrcKBDhAYoWQwbAKOCjSJEkOao8KKDBgJcwY8qcmcAjyJMXV25EiFMkAZ49LWbUqRIhgaBCBRxF+nAo0Z0CEMycSrWmUqYPnxYEipXhTwFdGzrVuvVq2IVGz6IVQFYjQqlU48K0uvRs245g1X5VG2DsXQBp1QY+6/fuW7mI6fL9yzXsXsFs/w4cHJZy18JtDyOOq1gt47xnHxOOLBmw2dF1K5OWrHnz1M52DYN23Pjy6s+pbefGiplsa9cyYYf9zFe06tJ4dzO1zPu2bLjAgzNnSlxv7ebITStHOp2788xRo9P/7B60eujry7/7Pq16e9DeWn+LHyC8q3nas21nJ9+TP074T8knXn1Y3deVcfoh599JC4oEIFECRkcgdbIVh553+7GnG18P6hQhcBMiZSBWCGKnoIbNudefevGFN99cDYY0IlMlppehiv+haOOJ0L0YYnkVWpefiaXFmBSODLIYoIsv0mdkRTMiVSOGJyLpoI5UFsmkj09mFeR5Q+6opZUhdelQhyt96NqPPUUZ1JTvKQkhlnGSmVSGPc7HJk5u9gTnijdySCegPDb50p4n9YnTnzkGCpmdFaFZ1JZ6mtnQXQZc2BMDCGi6ogGlZRrVTc0hQGp6oEomKgIJtOrqq7DG/2qVqZAhZOutuOaq66689urrr8AGK+ywxBZr7LHIJqvsssw26+yz0GL6FgHUVmvttdcesCq23HLbqQCp/iWqAQd0a26135J77rnphoqQuut2m2658Xr72alBRTTRWSmdaFJYJP3bVb9aCoxVwPhuquhJjCbpKGqCPtzeYl/iF3GVgkLalJweDpqjxg4t7JOnjWL86MVjZkwxeBaGmSVrHicJslgcpxnzlTMzJHJIDV8p8YaQ/Zziyuu1jDLMOa+VdF81T7q0pTpXfCDJDpsMcdBWT+yZ1CRS7XPWQI8mdHpL72xRz2U2zRHUSh+Nm8pbsyyk27I9fXPaY3tXNtc0ev+NN9hDY53yyXEXPbfgSMMtNuBkE92i0Yi/Tfjig18dm9xg0g2e3XbnXafjS0JOeeKTH8e43qDPKbrplWs9uuSWD8e3lH7fefrnkdetuOyYW5z75rsn2HrYvBue+e/rca558sEXOPubtUeqNlTKI99i9ZdrJapanEa/cbjSjgoZraOB39aqCa9IvmrQtu/++/DHL//89Ndv//3Hho9AvdxqOy3/12qXqt5FLwCii4AGtJYAxfWuBiDggRCMoAQniMAEUgsB9+KXRBLCL0mtrSTpOwnCOjg9t7TEUE4CIb/MVhG02W54gXvdc1CIKJGw8CEulJ7nVtS5QhmqhjJ6np//vHemEhqEbUzroZbyNCAk3tAhOdzYDj+2vBYxUUJOFOKiiEizKcqsiku6Ioiy2LupuSxOXsQZGOckxjWR0Xi+kyHwSic8mLVxM0C0yBMbEsUipjFtSrQjDd/4uMPJkXl0JJIgf0jI0BmSdaSLXR0/c8fENFJ1j5yk7hIppkU2KY9Q0iLDuMgQD1KveZ2k5CDvpkdRjuyMhIJh46wXxlXurYxdg2XJZIk6WrLRlqnr2Oo0OUdJKlKVjGRlKHHZN11WjZe4O6QVgVm4Qh5PmkvCHiSR+clLCjOTx9ykMVM5w2TeEo5mXGPHtEnM9VRSLqD0EjNp58yvQZOH6rTZOznj/02VbI9f3xKU+ciCvvGFMEkD1d5bZMVQWb3loFfCn0QnStGKWvSiGM1otM73lgl61KMNWNVHRxrBd7kLXAVM4LwseEGTDhClLCXASlmKQdkkAIUNQMhNu2nEshiMKSMEmClN+FOkBHVgn9lpk76l1Er1lCXsDGcxXVe8FjV1PkxN5h+PlE+noVJENkVhVnm6VekFEnZUtU9YDTVWLj11Msrc2FnFmVbngeeq4mmrU8sq165+MKoUuqtYdarV2+HTl+v8KpAEy1bCktWwVESszQALVsYu1bFu5WsR5zpV4qnVsi/SaxPfmhzFxjKSdQ3sevAaHdFikbTaMe0uUetZu/+udrACYK0bYYvE3vI2rpsNps106xrXjvG3lEUjZL9YzSURdzPG3a1macZZRI6zsrdtbG4Le8/IYnNOyW3TWi+73cd2l7nfTSwnsWtV3D4XnkM9InCp69dTrnex2SXve/mJXNk+k7YxrKpz3ctdAM8yvZP1rw3HG1rM7nW5apSsV+8rXtBi1cGjnW4p57thDbetuToRVUNH7CqmknjELmWg+EazPtsk9CkFZTFE06bRGtv4xjjOsY53nCv9xdR/UYnpAsMHLwvO1MgpJrIDSUrSCho5gwDb4IwrQjCYFTVfNiFhhlySzCsrzJU8I+VaPJzE+ppwn1Xp56SGKVXrpvb/Zdx06zmtGcdt0rW25AQPml+j5g+yOc9uxjOcy8nTOTvymnbubIDbOU1zCnfN4AT09RT8tyVSM3t0TqeE/0rpF3pSzo/2c6QHregDJ9qdlxYwJhHN6Gx2Woc+LHSoofJnUgd60W1utKxB/E1W59rVFJ5tnJ1q6FXXudXgfbUUYw1qXtus1so9b4QRPKk9j4fDC9mjV8TMNDL7Fk+p/iw6c2lm+YZX2IRuNqYPfexfJzvY/x32aIvd63ZLGtjXjfaniT1rE0L7tGgVtL7l/Vp6P3vUA78zru/9S0c7G9K+Zrh68w3wdPP74Rv5J8ACCpkXEyXGqmlxczwe4oWeuKEP/60Vj1fO8pa7/OUw7zFHg8xSIO+PpkmeeZFV6mSeg+ukOzfgkVUK5YFJWcv+mvJFsizU33oZJ0c9mLYX0scuQhiQ5S7LuRMF5rNxO75aV7Yfr85VjNMa4RUvdS+pzWl4L3ie0KtnpQ289lNP2u1BhPsQ5e7pgC/c1nenOJ+63sKv9xfvfVe4qcWdaXJv2r6Cj7fi6854dmua7ZB/c8LVHk1V1/vydse35tN+68XbtvHNzDpUxW51aWPd7P5GO7o5f1jMm3Drbx936h9/e9Z3mOxm7bd8/z370lPe3RMf/eD1vkW+w9r1Zbe9uX2fbcLj0PDexna3gd9X2A9f9pKnvf93Q//uyOce9fRUPVxxP3e/m1616I+7+kuL+OfTvfOVNzbokZ18gZM+8MrHdcw3Ss63bNAXfLw3ffUnTwpVgA3RPQ5IMyS3EiBnGyKHKid1gd6hgXEScx74gSAYgiK4UQSVcyVIc0j2cy8VdAA0dEJngg3Igvzjgi34GRmidBABdizxdCLEdAOjg3DFgyjhg1InGxkyf7G1gGN3gN23bnNyhAkYdkrYevdXe55nM1AofVJofu03efh3ekuSheTXf3+3ecb3hfAXhieChL5VXQDof8u3HmLIfwk2hb/HhMHlfWUxh8hXh1yYeOKHXlc4KXwocX4YgPZUheOXfx1TiID/J3pwGH5naIWMiIVrGIWrZ4djxn156ISNeIlamIl/aH/ud3xp+ISgOIaHGImJWIpoyF5qqCVsqH3fxon0pYcs4Yhm+IZl+H+Q2Itx2CK66Ivlh4hdGIjTNogfNIzFx4vv94jFyIp5J4epSIcTNooGqIiCWImEWI19eI3GCIiTuIhgiIqyiInrR33bh4e36ImWeI6hmI6auI7amIzcmHEwCGOGN4H+lHIy1nEZiINFxIErMoIGeZAImZAhyGQTFFL/Y0E2J2T5+HE994IwhXMqqGIyWC80OIMvklMcFGX70nQZIoQhEXVABYR4YZIWgZJG1WARuG0xuYnsuGFu+IvP/xgUMBEBPNmTPKkCM8FxEQeNZJiTxFiUpogUh6IATNmUTXlVQmlvRLmKwCiJzpiUOjkAG+CUXLkBMhGV+/eNbYeNS1iPr3cWAxABXLmWTQWWjheP9EeWVOiKlIgVA8ABa8mVHBATbrl7cJmEcnmHZhl9YWECebmWEQATfZl+6BiX4UiKXliXTCECh8mVIrBTiyl/jQmYj5mNdEmOTFGZa2kCL5GZezeL7CeOV/mKQbGWIHABIICYpcltVSeYn7mNYpl50mgRW+mUFyAAF9CVikmb2GeLNoma6pgAXGkrXKlUptl8yDmPKsmZu1kRA4ACTsmcTckCfEmcM0mPt2mPuf/Ze/NIH5TJlNqpAJfZnd9ZmzQ5mAj4l23IFy+xAk2ZnonJnsRnlTiJlUdJlUaJEzCxARzAAbZSoF75ld4ZnYH5nuF5lmExE7YSF89JgAzamWX5oITZFRKKEBS6oJs5nyFKi9o3EyMgACPwoe1ZnDX5YfJJokszEyUwAiWgovvZipEJmlMJjtX5ECcHKxV6kkGaNvzIERVYKgIpgQFpUCqnkE76pFAapRJFZEC3glVqpViqkVcqLjZ4Il76pVqSIWIKptlBjWF6pmgKM2Oapl1apmaqpmxqhGTapnAap276jnVKp3oqp3b6pnt6p93Yp7GYp3/qp3xKqIAaqIhqqOD/saaLyqiQmqjy5aiHWqjCOKeVmqmSakKU2qiY6qmCOqiWuqlw1amRKqqaeqrm+KikWqqfeqmhuqqjKqup2qp4YaqwyqqoCqq6Squ26qqxiqez+onBKqy1aqvTqZLJ+lu4mqvDuqnL+qrOeqy+qqrE+qt7KK3VOq28+qyK6q2JGq3F+q3Ueq3gChXYyqnaaq7laqzd2q7kmq7i2qvs+q72aq3uiq3zeq7qOq7LuK71qq/M6q/oCrD5yq34Kqn7Cq//SrD9Sq8H26rjsqU6R7EVm6VUirEzJ6Uc27Ee+7HG4mM1ty0YWaT4mJEZq6UrmFIWuZHxUlPgkaQLoS8h+YMD/+uwk5p0JMSSVDZ1AeCe4MmwBYuzW4ihl2J9UMSiEBuvCTuWRhs1A/hKs9isuzqOuHmKn/eWqsi0CNu1/cmaFaZ7jPmiVLutXyuZsKh/WmuNDbu0bauh8amMZzeUu1i1Xmu20ViV52d5azueOUu0uWicLiq3sUe3/8m1dpu4eRugAii2mkm2Bou4i+ufwci3frm1byu0Dwu3TUi432e4zai4AXu3SAm2lau2l8u2Q+u2q8u5nei5ZXGjx9i0rau5CtigAeCzQLuwtputLVpmuAhXsquaohuxeFu6aItf8XeaI1q2o3u2Oqq8lju2mFu790q6AEq5jbu80Nm8kZu5yP8Iofc4t1JZt8drvM87uaa7vRQ5kxAoUBZ7giarERP7UgSZI/ebJCC7v/zbvx4rshBJsik4vwZRvyp7wETGsi3YQAw5UkV3MEdHkt9rvbRLVDI7EgjBZZ+ku0rLr3/LuuTJYjTEwd/Ju9dbvDxaKiOMtHzUwb0buIArjyL8QyQ8tRO8uTmaJNYWHDXsvTF8q4LLNDscEwnQw5D7w6aRfUM8F0ZcvTh8wuebwumxxIfSxKr7xBX8wa5LM1RMH1bst74Lwlqcw1fSxUXMwjJpw0i8rHZjxl9siJKbvnKcvXHixmhMdS4MxXMcx3SsPisctWFWwjcrxmG8xRtmx4DsdYL/7Lzo28hSvIF/7LjMe8SEDMPwuTGILMndS8keXMhknDaZzL0W6sOVDKyXPJCRLMpSS8qdbMmGvBahPL2P68RjrMeO7LQWmMqyPMm07Mm2zMePXMe6XHLuO6RJQcAFYcApu8wzl79l/KMM5b/SPM3ULIIAnEARWbLxG4PbrI8XOcAKPIMPDFQRbLOMDMzgux48u3QqBDBvvKPpjL3orJt6O42qHMhqXMpA/Lt9Rr5hCcfxjMIBrb7JG7b3rMj53Mqm/MrA644QV76HO9B7LNF9HL0Gvcub3Muu/MIL/ckbWo5Zm7pgvNG/TNHBXNCnG9LUe8W1nMW+bLXiibUHB7r8/xnFJn3T9My4e4u6Kz3SHV3SFLyaKM2+PD3LLP3S8ozTIfy01ZfIhbfIN9zSSZ3T2rvTKm3UPr3P+pzEQdzPhQvRoWvTQS3QVL2+Vj3TYF3TEz3WYr3UPRoyd/yzeezSJE3XMszUuRvXuzvICq3VDO3Vn5vWOArUWDzVbl3PreTU1wfVa8zXHh2341vACOEAQkLZYeEAE0nMyEwQynyxKpY0mI1toV3NpF3apm1jzCy/Gqvaq83Nre3Nmz0Qse0RB1Dbtn3buI3boELbud3bvW3Cdn2r5OLbxG3bGTHcxU3cx53cxT3ORlXOBwPchi1f65yD7Wyz1f2A72y+a13YX/+bAQKQATFd1faM0aPMyRztmAwxARNgK+w9ARrQuW99tIqdtIy91Wz8EBOAngjRlC4g34i9TJp83hr908FNnQFwn/3dlBPwuvMNtQO+yuhN2Le7EPvN3wLAlQ4e4Ax40E+d0OnNmQ6QnQvelBTQjni93RHN1t1d4SlA4hnOlfE9uA/e1BGOz6wc4oOhActZ4k1JAsc5jyoe1i2O1PhGAj0e4zJO4xwO1/Xdwvfd11xdOSfAlb8ZnFz5AkGOu0Ou1rfM4oeI5E35mrG55Eyu0+Vd1Lx81HU93UXL46KpACmA4jWe10+exjlO4UUbABQQ5xuO5ol94wid5wfOHKLZ4HT/3uT0LegfTuhuLorrXZkADugC7uGLDeJ6DukMQQGc3umcfgKTTt6Bbun2jemFDqPq2OWDferZl5qVbt4SXuB+reOoLuQzZ9n4getYEdqxLRCdzdqfrRahDdpsM9qnfezInuzyk9quzeyw3c3t2+sA8OvNjimf0QDYnu3avu3cPuusHtVG/oTIzdy5vdzk3ttJhVPK6tiZbuAtkt0M4ZL5ku7aBe5tTtbeTdDRC97ineYds18qau/uju9SDb1XKwEPYCsPsPAV0OEDVu+Nfc5KXeG1JQELsAC2cvELYAEOPycATxXfIvDe/u38rCEMoPEZr/ES4OT5BZMiP+X4fXiS/9QBKI8QGn/xLN9eEB/zEg/mJz1+FXDzKa/xD7DoOk9eLy/dBL/nAWYBQm/zN78AHgDhR+/yEZ/0Mu86HxD1Q6/xHUD1D4/0Vz/2p9yOTv/0AhD1F08R9C72PI/1rc4QFn/zGCAAGKD2X2/jVX9hpKX0bV3w+i6IZ3/xIYABIaD2U6/3YW/1b0/2f80eQa/2UQ8BRr/4fA/3mF/yuUHzkq/xOW/5ebXuPZ/vRa7pi3bynb8AK1/5Ho9bmd/4Zb/lct/5HP/5rb/zUu73pX/XePYAEPD7wA8BDW/7/+76jk/rcR+8ePHxUxHyx9/uIy/UFp3Sw2X8sJ/7WV/n4QPNsv/SApndjyjr2c4e7SdF7KKt7Oif/uqfLOOv2a9N/u/v/vFPgd/vT59x7r292yWB/+XO7iSvpgBh4MBAggUNHhQgAIDAgw0bAoAYUeJEiQkZBMCYUePGjQwOWOQYMiSDhBRNnoRYEuXKiCpZrnT58mTCAxdF3sRIUkBNnDgZyKSYsGdPAkJvZhCQ4WZMoBOZNm2pEGpQqVMrCiAwFGfCrFpFWgVg1CvHogJCSoAAIaGAtB84Pp0KF6rcpnSBch0bEm/ejWDF8sVYlmOFBYXXFl5gM6NdmYxfOmYJGSZWwBr3Vg7g1yzmAII3wkB8uHCHjZJRmp5ZFWxY1Zq7Yr5cWTP/585/A0hAvEB04QqWW1tFbTI41dVRX1eODXg2Z885c+tOmNuC7+Ipf8e9Pjd7Xcqck/Ndjrl5AAjPdxeWsHj73fWN2z9+H7k77PmygW8WL9bDc+gC+KuvjrUAh3MqvsmOA+y7vMKrrLnyzIvuufQCIPCqAQ08DcPUEORLwbEYBKy5/Z7DQAAMnosBwOoqjOrCCznMy0OvQORrvAeeCwGDEJ5zCyMWrXNxRQ2Fqy/BIsG7j7bxAuCPv+lULO5HAYV8kTYZtaIxryVxaxKx3qBcTUoxh6QKxrGuHCrLsZYMwIIuFyCNOirnjJLMAs30Cs2e1PSKTcK6VMxHOy2kM8xB/42z8sgFk2TONowkeCBSSR/oUc46g7xUSDy10hMnPrViM8ZDgSxUsyq9U/RDRvPDD7lRp8w0VkM3HarTmz4dKtQzXx0T01kTpXXPqQxIyIFGBTC2MgcSMqA4YgVodrVnowVrWmdtDWnZVLXSdi1vvwU3XHHHJbdcc89FN11112W3XXffhTdeeeelt1577x3OWmmZ3Rfaa/3tl1qr9K2W34Cd1cwhhQtqlqaFH+611PtcZOhhh0qq2OKDNAs0L49AqkwnX02VGDueQqap47F+WrVBR0UdeeKStQt2q22xbDnEl3eNGbueaQaWNlyJ2jlPXo/+mbual7o5zZxrLJpTpP9n5u5U+pb+6mkto651allJrhNrvZoWFjslue4p4q9lDjtozobGSVejk2aPbvfEfotsT7VeE22b7YYPcPnwLk3vW/nu02+mBZ+M8QwJ9w3yjOC+SW6pHU8NcyIlF5TzzBAHVXGR1DZU8zLdxoxykSzv2vQCXb/Kc2xDUj0k1tP2unSq2ZPd8KzNPjZR2Fvc/W7U7QOeVeGLD5z5wY9XDvRcRR97eFLXxq53z2sni/q8rYdV97ZR3V56ols1EnzSXYMeyeRdRr/D3MEW/1fyhR622GOTBUxbgfMHWMECODCDCZBz2jogvhS4QAY20IEPhGAEJThBCpKLgAME4P+aQrD/C2pwgwXs4L88CBQODithGmtIw3aCwoasj231M1XGWDgQjM3QIBzDzMcEoDKviMx5jfthhk4GGJ0MkS8se5/O4gezIGauiZtr36KSCLUl8uyJxLninaKoqilurYpzyyKhsAe0+73NfHHzXuHUN7/7aA9/XezbFy8XRuKNUWlbnNEZK5dGS8HwhfRjXxlTp8fV8RFMgPxjIrOHR5zBMXFybB0dr+fHRQoSedo5GyRxt8Y1utGMjgydJv8myfAhspJXeyMmg4cqTpJydnkrHyinJ8rFuZKNp3RVLFWpPFbaspOMdJosz7c8O9bNlp4cpDDRSMvR3VI7v7Rk9JS5R2ZWPs+XxwRm2XYJP2JS8pnYjKb7tqnEbprym8U0XjilOE4qllOR5/TmHdXJxQ8ia3+Y8Z8I9XmwfiXQdxxxQEAAACH5BAAKAAAALH8AGwDeAKUBhf7+/kc8igAAAACAgNDN3Ds7O+no6dvZ58jZ2wB6ev0AAP6LAEpAidHl5UM2fv/HhEZbW7AZOo8kVKdlQZBcU//ZrClqaiwAACwYAK8mJmUyc4U8PMt1Jv+TETAoXFtFfKoAAKtdAM0YGP/q0QAWFnksZGpJSWdKcrNrOMMTLP+bIwBVVSggFXxTYppgS6YdQ6qqqv+nPf+rRv+9bv/Ff7vS0tQOH9QOINV6H//hvQAAAAAAAAAAAAAAAAAAAAAAAAj/AAEIHEiwoEGBAgQwCMCwocOHDxkcSLgQokWIDBIe3Lgx4YGKF0MGyCjgo0iRJDmqJJiwwYCXMGPKnJnAI8iTGFfqTIhTJAGePS9q1KkyIYGgQgUcRQpxKNGOAhDMnEq1plKmEJ9yBIq14U8BXR061VrQaNiGZs8GGEt2YEKpVOPCtLr0bFuDXMN+VbtWwN2yV9WmPcv27lu5iOny/csSrNq9gv0yRhiYcOWwhdseRhxXsdrJlPlCtgwawGDMl7tmJruZ81TPdkHn7ToadenTqlNjXa21tWuZsMPedny2du7hdVEnPy476u/XuLEOFz17t+TJ0ZlmR8r7qe/nL4N3/53+uLr264y3B1XfszvR7+DFS29Onbjt5sut5z8/HC74uezhRF5x5nGH3l8BnpSgSO7t5Nx/AOrG1IB6FbjegYZJaOB+BvYHYYQcBkUhbRa2h6FmGl4YookefjiAfBPSV559zGGXookr4tTgSvA9ByNSI2JlnHXI8bVgSDsW9eCHP4ooI4E0EomfkTfqeCJrS0LYZE9BMjUkf1NGlqOCV/aW5X9bCvhkhVGCaeOYDFZJZotMHnlRl0h92WGYlsGJZJnenRmfnRbhGZSeFxYpJpV0akkoRAZMZkCJJzGAAKVkRsrYpFHdZB0Cnp6n6V+cIpDAqaimquqqVoEaWUKwxv8q66y01mrrrbjmquuuvPbq66/ABivssMQWa+yxyCarbK2j3lUqAdBGK+200x7wLLXYUnupAM22xakBB2QrbrTbgjvuuOVKmpC552ZbbrjtYovAcKEGJRFFZ6XUnElhkcRvV/ra+C9W/tbbEwOG9oQoi3wqx2jDufl555oktrnnm1RKbFGSW8kZp8ZZUSwkpnEq2ufDGC/6mchekvynyQ5HBnPEi7Gcp8tJzawfyul5/CfIDyWM08JW6nwe0GIB+p7PSSHdkNAnET0nxDvLTPXRNdtYH88IMr2x100p7aDTDD0atM2H4vy10RtynSHZfcENtU9qh822im6jCLfZDs3/HZLUJV/dttUpn7yy1jPmjeXeYD/EMVSMy422wnU7LjaPjSfN+N04Zt3z1oT3HLniZo5++OeJh9616aQJjvfpXYPeeuExzy56xp7Hnrrtq+Ou+tu+x4Y4lKQHyvp9tNMMe4ayI3+7yrwDD71wkw9dedKcW7m5652r1S1ZnKpl6fVocatup5G5atn3WpVqsInqo7bs/PTXb//9+Oev//78998r+0+5VryqJcABkmtd52OXAQ/ILXgtEFrp2tS6GoCAClrwghjM4Loc+MB5Ned9lZqIQvL1uIPYhIQlASFKSogXAbjERVYZGFMQVr2oka9sl1PS8WrUM/84KnNPqyHd/yyWKO5pr3hL8yGa+BbE4bEJiWMLnvO6psRBAZEhfrsI4F5mRDJtz0ZV9BETsSjEv92wL9nzIhQxF8bfpOkkWbTIFnPWxY+tUUltdM0bRRJHiMxxbXX82R07lkfO7DEkfXzIH+0WyKYNEiqFTMwYA5BIhyzScmm04+9QFEm5HHJiTqzYI014xbiNsoWd7MwkK+mVM7IQMDuUEhhd9KJVllGLrsxhx2Lpph7S8pOFuqUcc5lJQW4SS6msii1DOTIiMix5VYseJ3+5TNQR75ilkyIPqUjNUrKSIZfEXiO/9kVfwrCaurumNBenTVmas07eFKYfiTnOsJWTm+eMJzNb5v/MotXTcvfMUDKho09rPhGbxmtnL/EJT7gBkCjhy9e2qPRQnbgvfSqMU0VXUipWeZRVb8non/xH0pKa9KQoTalKV5qsjaqkVBmMaUwbAFOZ2tSCCJRgAx/IQAV2MKek2iBPISjUoXrQRgmgZQMSklR4DkeGSCkYCp8qUotItV/DaeqHtqXVJeoScgq92PMMJ7yedfU/XM1nMR2J0KXxEkjNOSt40urUf2rulLAMq5OQSku6/nCt5MRrY966V7P2lalqtWv5Aqo3vXIprocVgFzd+MrBOtaf0MTa8lA02d/41auAtadg3VJKdGaos675rBVDC9DRUoawj+Wri1Qrxq//khK2mB1r7agn260itq6ZHdw6szm98UB2tr/9q2JxyFh2Fnc+vYUQbSlr2xbidmrBfV1Zu4ZazkxXj5Ul7XUDl93ubfe0ke2uJ8P72stiV7fKOy9n05vY8h6xrVF8boyii9bkgna5psQv5saLyOP6VrL1hW80pyg9svLWsMhFMHAVrNnhJlS/cOXvXP27WgBPkr2mKW1BuUvfCfcOw0W0rxq9d76PuhhVXH2xi4HqrJBiNDIu5chFLRO/3LD0x0AOspCHTOQizyrHGyngAq31lqESIIJB3alRi8pTKNeYWxS86U2p3EF65UuEVcUIiE/YLzIDbMwu7CZU7fXNAISz/3ysvauA8dhNye3zZv1874kdvE2B1jl3zNsdgxuL4me+84d2Nqgo57xL95L30F5NdDoPamG3OpqLs8ynpAOtzkE7l8/uZCiiAY2i5vWZ0KBeqJ81TWosmTrUDd7tqZH55826WtCzJm6qxSrqSLfaTK9WNaplDetpstrWwMZ1sT9NbGHT+tjyvXWnc33hXacY0lbcdKmV7WxdN5vXq27orwMVbHAPO76eNtNAaWLabU972d5GN7WTWOtoJ/vd3a72t6/d62yPe2nl5nes5Q3vQK0bOO2WNqXTrW+C55ve0H7wpBdd6fxa29D9rq22FU5xhlu60LnNOHUdej7xTRTH5//r8afCHDYkH6SjMv6ojS1j5Jrb/OY4z7nOmZVyJzM5Kk62sre4vMB3BZ3GQ5dylYluwKP2jOUOudcIy1xdwKz5YGYmGJqvjpOrAqzNb8ZhnBfr2hATGJSKbmbZPyzisTP33w4KOMYHvuB5W3zfsU07P9cu4ubGu+7Q1Tue+X52QKpYk/YmN7fNzWyHM/7vFZY4pxdu9wFfmo6HN2biAb54gZ8b8A+/u+MLO3G1MxqsINcz3SNv3DunLc+P3jPeQ7564Ure3ZQv+McvTvvPsz7wpd/76W97ecNT2PatF/zrCV98RmaerZuPe+fn7nvk6170oM+w8ikHe0wfX7sex/7/7/e7fet1H/PfN2/4LZ96PspTkfR8fmCHb93mn8313Gd++70v+9HnPfiDR395tX/o13/ZR3qT13GVp0P2J07yJ1osplMml0suZxA7hhoqJyopB3VJk4EGsnMgGIIiOIIkOD8VWBDf0nNTZj465VNFx3QDJHTgA4PxYnRGNRxeRnVUhUJcVylbx4EM4XUEg4Pv54Dpd18Vx368B0fN0WZoVnjOd4QrFn2Y44RVZ1kEaHwGOH7a1zVW6HYBloQMmIUhYyNf6GFth4YbZyZn+ICtJYBYuITuZ4ZFCGdqqH9yWGB0iH+913gHSH1+yIUIiCJtKIWIt35jmIdo54V1KHZ3/wiH4tWATdQzhbiF1hd6Sjh7TLiH5ad61Qd+C9hoZHh/lNiIaPSIYiiKihhMnAiAnheIl/h4DfeHatKKCXh9meh/fQh5sTiIWFKJtQeKuJiImjiHpciHngiLwoiJxKiLm3iMnRh7wah+oYh6q1iG0OiKgMiLyyiLu1eMepiNt8iMqgiOBTiNSBgWJ0gQKSiBKKdTHngh8Wgi6zgQF5gb82glJbiP/NiP/liC9SgQSmZAP4cAR8eCUeaCTUeD7SKD7cOQ52KDVZaDAANmU7UvQDgSWTdDP8iDGUlDyDhEeGiOWoiOU3h7HGd6qWiNJBmFltiNvnhvuUeOLOmM0viJ1P8IfOMYgCtJfKNohC+Zk+SnjeY3kjbJfyZ5iMlHlDZ0fiWJk+lYjT55jaTIlCIJie31k3bohnJGhUoid7s4i4K4jWLZi/+3k8uHlWYniY7IlWSHbIqHb94ofmYZlt94lIuIlvmnlmznd2UJk2eJewo4jOWIl0+pjELZhXpZlHzZd0ZJi89olWbklC6ZlJqHiIUJmcYombhEmZiEiphZk5oZjpw5TJ4JlJYJfaE5lS1ZlYvZlI85lnZJl4BZi9E4mbFZl8nIjYkZk3E5k3OZi6N5jlB5kkv5mlfZk/WnlW1piJfZFQEJABHVLye3Pht4Y9YJjxnZF/lIJv/4neAZnuL/iVJadlM0hXQziJBJF53tmJAJ9J4tKCnqJRdLdYWk1YT4aYuEmJ/i2BbzGRfbQoT9+Yv8yYj6SaCTIVcWAAEM2qAQYAEzEaAFup8HyoYTSqEDqhVnlQAK0KEe6qFnJaEVujQCmqGBUqIGyhhntQEf2qIbIBMiaqIOgqIZQqMYmqJ/0VUW0KI8CqEwEaM4eqM1eqEIKqMzChpdlQE82qIZEBNAOqRGWoVEaqEjSqJIChMQsKQ8CgE/imZTeqJfaqVVeqQJGh4ioKUtKgJN9aRCWqRB2qZuCqVRqhJNZQJouqUvwaZxuqd8SqVz2jE2ShZNtaMtCgIXAAI8uqZeOqZS/8qoShKofpqjMMGiH3oBAnABLtql9kkZYUqmfwoVkAqmZRoeLQorLapVehqpcKqqqyqqn0pKpdFVduqhpuqhXKqpoeqpb9qnrrqrrCqoMZEAZ9qhtaoAIgCji/qqLZSruiqnvvoUZ0WoClCsPoqrnfqo14qtjgqo2coRcrUBGZABsBKuJhChyfqsYqqsgMGsjaqicQErAHquzjqvrZqu6Nqs/vmuCRGvmxpi3Qqq/wqrAbusV0oVJCAAJMCv7Mqt2wqwDSuwD2sQqLUCJLACCjuwjbGwDKuuGYuxBBFzH+WQAYSeI6ue6wmf7hmfKhtl49myLvuyMDss7NlkQ1WQB/85syabnji7sxz0gkrXZR/0Zfiigx57n0VLGT24QimUL2AXfxxrtBE7gFTZN6YYdqcYtR2LtZHInJRUtU57r+0Kts04nKx4m53JdxrrsM6pmscpmCq5mgSrtZwKmm2bksKnnOt6tP5KtzrptncLt3krt3vrlm8Hl5wnl6/4q4rbq6k5f17ZMWC5m4xbr/iKmFHZt3bLk4CbtU87t4Qbhigpk4NJkxDbuYO7to4bur85usGprab7hGzZtSGJm42ZtqUblJc7lMhJu3jLuWK7sbhrnJgrum8rlXH7uv3al3CHOZF7k7xqr/RqucKru36rucYbuMgLhglHvH97vb4bvYv/e5dki42lOU+nuZXZq7fKa7jSh7hkCb2UG7aNC4FngbMTeL44tLMoe2U4251x4r8jFbMCPMAEHLP963MDGYMkW7L6u7L827MLiWXlaVMUSTAWSbSCK68ZkrQhQRIvxCRNi79Xm74ZLGIHF6whjLbqm7wm/EspXLsrrL2C4iMv3LtQS8KoGzYnPBc1vLk3/LtqG7z/68JeK8IaHL+uK8R/ssPh0cPe+8PgO7nSO8Qw5MSEGcRRDL9TvMREPLtnC8MlLMNM/CJWTLrHC8S3O7+WM8YJUMatC7xZXLm8aSVs7MaJK8XhK8d/CT9dbLamqcJhjIZ1XMSAjMNKnBSD7MV//wzGhqzGHdjH5Qt/RszCtnvGjlw+iezH5lvIaGzJxUnFIEzIjNzJ2HvIX5PJkWxJXxvH8ovEmXkeqLy7X2zDntvIn8zFMGS/EkWB+3uyDpx0AIzIIOtRBVzMxnzM/HjANZvANbjAEOXMFgXNHAWR6ELN4iKRQGsjH3nBZ0bJK8zBFyGEHLm0WCXKtHy6pByHEOEBAuABSjm+rlm9aXnORxyIEiABsHLPEqAB9NuaVKvImzzKrDy2JUCsCeGhNtDPhkm+srzI9OzN0ESrB+2hEvCGUzuJqdxKk1zJpTxwEiDRAtCiFu3PGN3QAf3QHK3ODvChxdqhL9CVJE1GAC3JnP880OUYASw90R/qAG950TKtyTQt0K5sjRpQqjrtoSnQ0zEtu0Ctyhsdw3ySAkYd0jzKz6C70PGcufPsw7WczlsbAAVdqZfKozeg1Fj9z02t0TU91Kwp1R5qqIjaolZ91fCM1hkNTqvM1p7MbEV9pwoQATB91iUtz3uJ0iu8HDh9pzxt1nU92Fpd2FyNzjYtmnda0YHd2D99126W18+rx+L70Vqq0JjN1JpttfWcx8IZAC8QAazd2hFQAqItm5Fp0kFt2CUMhSNdt9xrvVecxnottUu9vatbvL2916g9trL9zkyBs4vNJs2NFQ4gzS8l3TpG3Uk2RtF9RdlNNtmNzN7//d3gPWQNnLL828s6a94PmbPpzZ5PdQDu/d7wHd/xHSkeId/2fd+njcf6TaLgct/+/d4a0d//7d8VPEPcrHUQbctYAs5WtZFRZcfv69kS3spbzLYOwc7uTJq07dRr3dkUPsfTSwEUACsiTgEfwNCEzZi2reAg/s4UsAALACswvgAcgOKPreKRnd9a3OLPyQAzLuMz3gJZvdtb/cRdPdm+XeGpGwAo8OMJMeMwPuTD3b3F3dG//b23rJonAOVAPuMuYNcbrtZC7eFJfMmFyxAcwOVPDuULcOKZHeZ4/dSBfNvB0wJs3uUzjgKOTeSQbeSSfeVQnOWOm+ZqLgBsDuMV/wHhs/nh+z3hyC1cLw7lGCAAGHDoNf7mKQ6bY37ccCzo/UzoMB4CGBACh+7mpA3nmy3nLN7oqW17W37obD4BYJ7pyZnjCe7VWclnTQ7rM34Tii65O87qZe7puf0BvA7jFCDlhwucdxzszu7or2x9LsDrOGDjfI7jfq7j0C6a4zcB3v7t3n4C1j7lvG3GVk7mna7ksa2YtM67tp7Sga7uuT285F7kVY7l6I7FxH7Zy30+z91M/44U2T3e5f3L6cnd2B3eCr/wDO8/BO/LBU/eET/xEE/x4DMcDZDxGr/xHN/xR57vSc7p+v6LAj7g9h3gJk/gBgYh9QnVc94zDC5m5P/8dSvfX4ACAwUAA+ku8iEv7NFem7M9XxF2EBXwAA/AAgLAAkafA8bt8yPP88vp06cu9AdmECPg5Ia+AB3Q9M/O6F3/6EC/meg19AURA1g/4zNw7lCv9k7PmoKN6Vjyn1QBpBVQ6FA+Avi+9nnf9lG/1FkVWQWhAnY/4zKw91+/83wP3G8/9XEP+APxAHe+5jNeAfGu95Wf+Oq8+H9P9gLRAZGf9TOuApd/+E+P+V/t9zW/YddBA4eO5zBO+X8O8ly/7W4/2ptf9QPh+ZJO6Wwu+h8v+2xP+rWf3BpO9dI1FqwP5aJO6lAO+7Fv+b8P/WvJtbd//Aei+8de+KNP+7P/7/W02ZuB2ficLxB1f+wLgPfb7/3qP+zyzu/sLv64TxBmz+tpb/jcH/z3n/m2n/rP8aRXD+sA0QHAQIICBBBEmFBhQoMLHTps+FBiwYMTJxokEEDjRo4dPQbA+FHkR4sXBSQYkFLlSpYtEURMWOHBTJoPKiiEWXJhTp0MK/bE+RMowpAjjWosenTk0KAoWz59+lIoU55Up1pl6jMrUQEZlY5M+tXjVopOoZ5NKZUsgKpD2wJ92zOuzrBiOda1q3Et25No/aolO7ekYIuETe7FmzexXQNkDRhMEFnyZMqVE9Qw2DjrYwGaN2d2DHorZ89MOXvNu9HB4q+rDb6GHVv2/2zatW3fxp1b927evX3/Bh5c+HDixY0fR568dumhpEN3Hi36M/To1KczB+q8Ovae3OEKOBBe/Hjy5RsbLJ9e/QHDEts/fA/xqtuDBtbfD9/QPv71iAUwSG0jBtj7L0CNGIh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Qmdt0ZmigaR1kTpOBt0ThMLFZTF6GM51PXOd/rNlMdj7TnTXk5g3ROf9PcOZTnErDJxal+SFb9lOd14xMNqkJKiR6E4P+HChrFsrPBdHznQeNZ0IRQ9GH9imis9SnFiv6l47K06IgPSZBCcXQlZh0o8FMaTRFmkyS1uWl9czoL08aU4TmlKPt1OY/4blTmH7TpxgF6j2FKlH+ybScK41RS2EZ1Kmq8qJD1Wk3eXrTqtoUokjNKkrD2lR7jlOgIXUqWdNK1K0aNS3YMSNaQ/TGuqxRVHYMUF3TclduldI6e/UKugpJ2MIa9rCITaxiFxuwwC5FYZS02LkmqUnKimyTarTsxzJ5WcmqkUx/1crMtoOzy+jMlWQqbclOmbNU6qgic52pWjX6057/0japr32RVWHrUK6C9bZize2XdkuRL5ZVq/v0qHAbStyJGJetyB2pcpc7yua25qmwiqqjvkpdVVkXAM9VKU3POd3uFpep0B0rcI+r3qLW1rze5S6jsDsm7YbJoPBlrnyXuVbxzta9uM2vKr8bXtm2Nbm+FbBz0evfA0s3wfPtr4EVXN39VpO+urIvrr5LJgL39q0flTBUKazf8jYUq+y1LYCDS+LNeBjF6VWxW9/b4tiO+L8zDvBvV5ziFoOXwRNub45ZvOMh97jGQL6xg2tqYlXBuME+XvCLMYys8RaUwyybsoiza2WWWvgmjlUKRtCIrTqGdj15HVCYcdLXO3qr/8yMjbOc50znOtv5zu06JCIVuRBMehaOmqUYSjK2MYf82a6BjhhnN3tovp55KycTwKOr4trzqlZlCqnZEVM7aamctrWvtAhOdRxivFLNnFf+sku92uRR2q2LTar0gp8c5JKyGsL8NfWmNWwm/Np6qVrWtd5CzVtaK1mhtwZxhIWNOVlfd8v1nWiyaVxqbp2a1/3xdVeBrWpXUwrWbHL2j43NZYBehpzlXvKDwL0nYp+X3NE2N2XQHW91f4jdinL3rKlMNAuNmsjVHte1uyzVbot62qRetrV3TfDttvrg3H54sb89cBwjWNlK+feRAx4gfGtK389eb4y3fdYkpzu6Df/yOKpAPm5+y0nePbPxyYXc8Yrb28sSp4jGR17kmjPc4g/GOE52DmWaD0jltBJ3gY+t1JIHe+HDRjXOcT10hANc4QL/+c0LnvOJEL3WR2V27ZT+YWovJa5nfPMjF9LpzyRgjnCfy5rBbGY2tr1Oc1/JYPHM9777/e+ADzy78s6SRj/W8IfPCJwJfxHEi9nxbIY83RXvMDKxzPKXz/ycML/5KKtS89vhfOhBfx7Re34zpP+P6VWf+gWt3vPOJvvrOzT7F9Ueya2nfe5tv/sv3Z7EsWe57Hv/pN+76fS8Jf7xlR8p4zef+QIOvvOLNf2GVl9VyD8v9K2/fex3f5TXN6//9L//efIn3/zazz7qO8/+0be/9OhfsPrX7/76w//9rMe/6+c/fv3r3v+8B4C+x38gN3wCWHzxR3/2d3r9t4D7d4DLB4HPp34NeH8O+H8XGIAZiHsSSH0J+GPhV34dmF8VmH8biIAj6H0pKH4FKHwuGILnt4IimH0l+IAnGIE3OIE5GH0tCIPpJ4MxuIPchy2Lt3aMVxEtUYRGqISWdIQ5IXl6J3hSOIVUWIVWyEd6JjJ8loWMRnlNyIST5YVh6BKRdUll6IQTYQB3V0aRtoajZYFwaILncWkyw1qmJW5fx3QYGIc2uHUOR3U3kYczt4dyWIhGx2SAuGoRl4jg94Fk/wdvGSZtiyh0QyiEKgh0iEiJiuh0BheEfEiIh0hejAhxnNh1CviJGuiH9/VdglhvhgiKsBh2PMZzXtGKkWiJjQiE7+ZyUuNvVrdxOIiKA9hwq9iJOveLtBiMr5iKKCeKmkhVk2h2OiiMKIiJziiNVReNCTeNyziM1phqpggAtlhluDiD1Ih1F4eNgYiMRReL3niOuTaL7UhyAWVyrtiH3ViNqrhhxuh17Ah275iPyhiK4DiKFTGO/aaL8ueIZbeN2ViKBvmD5eiJzViQz0iK9ehhPriQxMiP4YiQLwePHqiQISePAFmLO5N25QKGmSWGLYmGEtFmHbeG2QGTALB3V/+Ykzq5kzxJhVxIMVvoZy4JaEOJaEXJV1BYeGZYWUcpWEl5EW7Yhqy0kacokJFCh3zxhlb5kBnZj1WJj2ApY+p4iWbVleFogBO5i14Jgr6ojVfHjWHJjAQ5jwHZdGYZkRyZlnm5j7cokuvolsAIl+6ojxVJl4Rpl+cmc/c4mAPJmPF4kRSJmPOmmH25lZXol06mkW0JkZApkZiZi99omI1Jj4lpj5UZl3XpmBe2lnj4j3ool6p5mYV5kqMJV645iLCZm6kpi525l8gGmMkomLp5mGLpkLIpmTFnmuT4meZomZnJmiwHkr1IkmxJnazygdJ5JwxJlS0HnTqSnWNjnS//iG0JqZcSAZ5oyZyRWZxvOZIwJxj0dpqxSZbO6W3euZl32ZtfOZ+gyZfLWZ8YWZqaKZ5SR5vCaZsvo5JuxJJEaZNJuHZp1iAO+pRI2JMWeqEYmqGK9ZMRE5RnyKBGOaFNmXhkyJQl2lkjKmZRCUqotZ0MiZV58Wl3GJ23uZjDWZvEyZvpaJybmJ9j2Z8Aup46GnQ/GqCTqZzlqZ6eGaRqKXKimXE1Kp83eqA52nNEyqPQyJlFKqS72aUcd6Xt+ZdaiqXNiZpV+qWZuKXHCJxP6p5K6ptzyXV4eZ5R+p9miqN4+pg7GqY9KqD3SaDkCTWsWKdJyqRwyp9XxYvaKYlj/8qnQHqnVIqOYBqYXOmnZzme4gmJdvprjUqpx4moXIqm10imB0moIWmo+zmlq+mkBlqpRzqg5lmdoSmn+imOpjqdsZqes/mHtYqemJqrLqiphYqc8EmZm6qqn4qsz8mqr4mgnRqcboqqsuqfcKJtaXFAJYetl9EAFPqEKRp53zp5Igo23Gon5SqiGpqu6rqu7MovIgqTD/qFSziv8lqvY4gtCJCv+rqv/NqvEdEU/RqwAqurkBqtrqcRApuw+poUCKuwCStuKyppU+mi4gmjnmSHq0WjbNqqyeqlebqqJtmsKLmxIvuxHXumkpqmpLqmz9qmJ2uy9BmnvKqm/kiyuP/psZGasyBrZNAqpj66soeqrI/KnqPqqEaanLD6pqlqpSprtKVqszaKswYLqk0asjc7si3LsTFLtUHLtEXrqX36qn8KrAU6s0BLp1ArpVL7sjp7Yooanu/JF/F5rGu7tUKbqNCmtlj7s067pAXLtju7p2CbpXw7uGXKtUsrqhZJs2ibtSXbtnZbt/bJrFd7dre6qGTLkMJ6qsQqt8Y6rIg7rdK6dJULpWlLtygLuUM7pE1ruE/ruKU7tXcbqin7tT0btkg7tkorujJbjOGoraUJvIVxriCKlOEahcerlORqIsS7du36vNAbvc/7rvR6r9X7ksXrlMnbeNuLhFRJsKH/K3sN67ABy7Dk+7As972/urtkZ7Gm1EqgNn+8O7t+G77B+rbipr7cCb6sa7suC181aL/725B9K1wBTL9dm7qBO6m3y4IUm7kd2WtYRpL8K7mH27+Le7avdcAWTLuqi7eUG7XA14MPzL4E7LrLxcEKDLgsvKxWK8IUpsIwu7odXLU8+78ODKg6PKtmW8C5JcMf7MGy67UZ7MMbTMI7bMKbi6vIB8RDvMKRq7hTV6v6W8Kje8IN3F1O3MI0DMWT+8J6G8NIDMHAusSY28RjbMIDbMZwi8ZW/LdRXLtFjMJpmL2P173eSr3XS5TLa67dmobSG8iCPMg6qcfWa6/Yu8ch/2rHd+yg5yuw/9oyj1y+63vFlje+k5yv5pvJ+wqxOyOVLZrEV+y+YSGjGfudl9vGlqy5+KuxsAvDQVy/CFySN6y1hGupczq/s6zLRDzFjGurpwu6u1zBUkyrv+yrA7zGrYzKwcy5AszKeYu6ltvMTLzKmbrM+InLtUrMMwzCYCzNpvvKYRzLCdzNExfNwuyshZvFQszFF9zLxqzByPzG9svGrUnNZ2zNZYzNjLrOOPzOXgzQcuzL8pzK3EzOiTvQ8WzE8yzKcPzFtfy4rpq7l5rMgVon1rq32vzLB/3ECt3DdNy4/mzLAo3QvFzMIM3OLDvSEu3O7RzHesrA/1wRwv+bnDXNF82LyA3KyODax32MroQc1EI91HRmyIms04usyMZr1Ed9Lgzw1FAd1VI91ZvByfza0S4ty76HyZm8yVYdabHiG0IhsaFMxiZMyqKFsZjGcsDyEwq6zZX80N4c0bH7K6Xy1hwd1/XMz6PR1j3yuc48zHqNwQRtxH6tJUga2DVcziZNurBMKmoC2NUs1y/dxR/tu7l82G4h2flM2Vod0OccwuMM2XnC2ars2Yzt0THduio9EZptKqaN1TCt2gvM2jNNGncd24O92Am92v5L0omR24k92c98zeis2F7x2nitwbJt2b49x60tEcqt2xZt3KINzqSNKNRNz4Jtz2z/LdxJq8/UKqhrOd3D3dnFvc/HTdzzAd66K967itm1at7hjdq9Xdu/3dIrQd/vbd8nfdkemdnuXdHcDc8pfduuHXdyFAIKcdN5kdOHHOFN3ZI+jR0Q3pJEneEavuGDxNRJjdRLzdPiKuLI68hfnTA/duKavNug/dnHx9WT7NVW7ckvA8rxW+AtvmBonRWmvNbMLM7YndWpLeS0LLjRDcxAns45ft9E3p3XreTTnOTIbc4u3thYjOAivdHMzeJUvm/rjd6kKbYE7tBEC91YjuQsXdez3eSPyNdlqeVG3Nwljd9mDtwrDech/d9WXrbyfcwGzeV7ftFhM6j4fNrprcRu//6bUs7evK3ntO22X27oYU7RuSznlT3XRn7mDW3WV+zdP57mj83mgE7nhZ3nm67G0PzkUx7OoD7aoq7MkX7Piw7mjW7pNpzpdl6zsy7ptT7qkK7qjK7RYl7pvr7mLkzXob4SDp4XS7PsYnHhO63U2juuFs68fxyTHJ7t2r7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",
- "text/plain": [
- ""
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "import os\n",
- "from IPython.display import Image\n",
- "if os.path.exists('/content/rware.gif'):\n",
- " display(Image(filename='/content/rware.gif'))\n",
- "else:\n",
- " display(Image(filename='./rware.gif'))"
- ]
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\u001b[36m\u001b[1mEPISODE RETURN: 26.0\u001b[0m\n",
+ "\u001b[36m\u001b[1mEPISODE LENGTH:500\u001b[0m\n"
+ ]
}
- ],
- "metadata": {
- "accelerator": "GPU",
+ ],
+ "source": [
+ "render_one_episode(config, trained_params)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 16,
+ "metadata": {
"colab": {
- "collapsed_sections": [
- "JaIw_5YaUSAB",
- "IFraNFqY6s7_",
- "4idyWUhW68oS"
- ],
- "provenance": []
- },
- "kernelspec": {
- "display_name": "Python 3",
- "name": "python3"
+ "base_uri": "https://localhost:8080/",
+ "height": 572
},
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.9.18"
- },
- "vscode": {
- "interpreter": {
- "hash": "31f2aee4e71d21fbe5cf8b01ff0e069b9275f58929596ceb00d14d90e3e16cd6"
- }
+ "id": "Xq_HhYIrWOWK",
+ "outputId": "370788bd-abad-4a14-a0b4-a6b2198a4f80"
+ },
+ "outputs": [
+ {
+ "data": {
+ "image/gif": 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54lWpemXnApanOmUO5ZXnQ2ZkY1lMV43TNdP0Di83y9nOevazoP2eL6MZrVJF81uSdV9q6bdaVLbWleo67TPN1QAE/9j2trjNrW4vOxJ4yWtgToXrXcXkzXcppAF0GogC8ma8sOrTryktFAKSO4DlCo6v7xxuYXk03eRa93XYxad2oVtQ6VL3u7hrLiTXG8eYmte7zO2dc29KXrW+l07oTVp4EzretPLzvmfK79T2G9HDgpW98wQwkQTMNQJvtL5oBWx0r3fe+D5tu2/96eW00138WnhpEP6ohMtLYfhe16rDw7CBc1piD59Yve3NKoLd2+IAf7hqKnYr3Tps4xfLd8YyjnFgFSwiBr8NxQHNMYZ3XGEfXzjESF2xgmq84BvvDcoHFvKEUdVk8CLZrkrGMou5bGIvw/il813o7sjsYjP/WP/L9PVvXqlc5AMt1sFgcSxLIKtayh7Tz7DMrHwQ4KlCeyq0iE60ohfN6M2O1pmldZVsCUAuWVq6snpeCTMnDU3ZChopvrUmcDNs2P5yp7j0Ugiq92Vl3ck5yjpO7Erx3NI0R9jWcyarSAkrZg33OqiynimtazpiNb/aPcEe6rB7+uvg+nqshivrsosa5mOPOdq7nrazS11tqWJ7p9om9bbHLe67zZWuX+6rtZ9d7P+GFNzpzm63cY3Yd+813uKdN5CHbO+HhlvK3G62uefK63UH3OASg/eZK0rvLCe437PGN3/1DWf7QlzYEi+wqRt+bcxJO+MPRni540qm2+kX5CX/FTjAE37vhQcZzfve8rfL4oAT1xwuDnjtMnX+WJ7v2eeaBlHOKzX0Rhv96EhPutJtlWmP8Nm1gJ5s0zvydKhP3ZRAd3rWqX6Arnv962APu6hUHfaym53cKx/5tszOdq9jZO1tZztvRRLquesE7Rvv0Kr/pWq7O8TvIft33ivubl0r/M0wJ7yI6SNYtxW83YuPOYkN33LEM1zyxva2x7ON8lpj/tafz/XM/d15YnOc3acH9sWVXXpmizztjd/a41OP9w1f7eMu/yvtyx17qc0+9LBecrKZKniKJ/7hlCd97p/7euLN7ty/V3zwBd57pEX/+JeXPrJXT/zWU1vlseZ+/1eLD37hi5+t5G+++ZMf8eXHGfLTh/bmD/9k9VN/+C+5Oe70T/OtYz3qfXZ1G1F1sEV0Qrd0CJiACriAmyWAGkGAPQeArCWBVjdLl/ZnDpgRBtAAHNiBHviBIIh62idcxudDcBd3Zfd2KMh2jFITDeBstVd+M7N3oNZ3A9OCIzJ42PdyNMZ+DkJdOKGD2beD/OaD+AGEMDhyQrhV+AcaSJhhMWh/8hc1B4KDRZKEaQd7TTgTT7iEugd822eEQ5JcQViCPHiGRTh64NGFZviFIzhlYvgSVjgaWOiFzKd5VPgnbCiDUoiHtwcXc1gnUKiEbShz81cWgZgVdViI78d4W/+IKXvYh7tHchHVNInoHIvIh/AXhmr4g2SYiZIIhh2Xh4AYiZtIgproiOfHhZ84iFkYfnHohK1oh40oinDYiUc4i4yYebtXfYtiaMCYKTHgfw9IjMVIgQWYgQAAgRG4M0PnjAwYjdI4jdQ4LMrIjEGHjDunjRF4jcaogd+4jOF4giv4dWOHL+WYgoSYihlEjulYTcv4jmAHeDZSTfQYANm0i6DXTfcITgPTalcVim+oerE4WOl3iiI4kCynfJaHhnfYi48oewc5ibCIi93nfrxoiwfnh2pDcBOpkVHIkZLjkd4XkhRpex2Jew3phkT4kB3iiytzfUM4kw45eRY5fiX/uY4CiXw3iX45+Yrr15MG+ZO0mJEZA5MgI5M1aZQtaZOHyJD1h5AbCZGr6HgfqZA6KZKhQ5IYuY9NyZQ9KJRWSZT6GHlHGZG+d5Vf6ZU0mYZP2X4r6ZJYSYnJsSD8N2ACcJdRMXTeyI0/55fZ2JfQaIDVWJiGeZiG2ZcZiI2BaYGY5pgYOCryaI5zNJlfZ5IgqXaW2XUquJn9aI+jBpQCR4My4Y8GA5Apxo5YiZQTo5QseXkfgAGyOZsY0AJmyZNviXFdeZsMxwKnwQG8yYRVKZFkmYocsBsfEH9aaTvQp5ZteYcfEB3A6XBhmZust5vKGUTHGR0rQJ3CWZBjiZ3e/ylWK3AiPAECqDiFf0h/IGZ/IGCePEEC6bmcxcOVcVmLHYQCJdAB/UEheIGeU/mSaGl9zrmUa1QAtPIfXTED86mK4Emc4tmgK6SfHcCf5uGfPgGgAXqWw5mWxdmH27kbNDCehkiK7IljmhiduzGdEsqhD+qhEZqQaEYCyJmdAtqhBPqhUhkbLXAa8mmjLiqWEHqfYCljLJABSJqkGaChLYqbJlp5UXmS94ejP+OacrmWwemWTwqV7bmjWemgQioSenlkY4oUfAmZyYSmkzWYg4mYbvqmcGp0inmBaUqnUqemASiZm3mOm8mZX5qZ9+OO6diZlvmZv2UwmDmXxNWPNv94mk7WpVIqcg1CHhrwfPYZpYBakQ0RARHAnx3AqRGQJAZyqZCaqUHJENCRAOXREzJQl6SKojv5nE5ZAT2xqj2RIhnSnDoaqV6qHFzBE7baE+5SM48Kq16qhQ1xF7WqEJAxqiqJqYoalI6xrAKAGrn6qlcWqwaaa6bhE8HaEzniNJwXoxuKpUCKfNNKrY+xHsTqZtBqriSKn8inrD1BAf6BF5FhKbpKrokKr9f2qzyxARjaE+shriearcdakbR6IgFyrc9aqtE6pQ2RqruBHftKpGy5redanQEQHbiqr9jqagl7qgHQrbzhqg9rrLxKlRbxAC77si5bsA47rhibpVf/KqsW96I5yq9/upoDWqUF+poaG685G6YWUaaUk5eYcaZ2mqdNO4FseiBFF6dUW7VWm0tz+phPW4Fbm4x4Sj9S1q84i0SCWo5iK0agOLIzyKg9K0dpu7I3OhKUKm+/FrZtO7ai1xATMAHlsbcTEAIhJ5V2K5pTKAEMwADlcbiHG7ipN7hFWXgNobiJq7gwkHIG57hlWW96K7kKobiHy39n+0Rva6prFgAj4LmTq7gT4HkDibmqyZMqgLqd67kMALiuJ7gCFbo3+52GK7sCQLsMkAKmp5Guq61FOAG0m7qeOwK327i5e7dDO2an67kiIAAiALyr27zE+7yE623Iq7gm/yACJgC8+qe7WOSKj5u3wAu8L8C6/mpAoxux8te767u47ou3HhS/71uuHJsC9csAEmC5uOtT5lukWhoC9asCjLu9BAy9QqulARACEjDBFCwBelnALqS/+Eu0kKuyDNxrGGyzTomwztvA3QumHjyQWRuZXbuNgim14xjDVzvDNFzD27PCdVpZBbDDPNzDPvzDO6wQQDzEQ1wqX8u19sO9YkLETNzDQtzETQwS6Zu/6EspUBzFAnDFTCzFmWtBGnwvWkzETxzGP8zFr6tGXyxFZAzEY7zGPGzGxutJVTwzbuzDbVzHcKy2lcS/Dzw2dezEWfzHQWwUU+zFc7wkgjzIiaeMoITcxQ6Uxn6cyHfsxnkMt3wEyXW0yJO8xpVMunuMyVykyYEsyJ0sv5UEymshyqJMAIX8yIfMHaosyY18xmj7yvQRy6Q8y3F8vm6Fy39cyvu7bcULy7K8yq0Mv7bcIb6Mx7qsx6eczBmzzJTczJbsttBsN9LMydTsyc/cy8Usy6zsyMjszblszOLsP6jcR9lMxsC8wU2KSkfswuuzzmHczvLcwo8VEAAh+QQACgAAACyaALMAqAAJAYX+/v5HPIoAAAAAgIDQzdw7Ozvp6Onb2efI2dv+iwAAenpKQIn+AADR5eVDNn6OW1SGJ1rJiRoNgHgsgWi4FzQsGAARFBCuaDv/wHX/0JerXQD/lBNpS3GOhjfJdCf/yYn/6M0wKFxOg1d3LGYsAABbRXtlMnRvhEeiZEX/oS8AVVUoKCirAAC4iCKqqqrbCxrPDyT/tFn/1qb/4b2fH0iiHka70tLcfRn/rEj/vm8AAAAAAAAAAAAAAAAAAAAAAAAI/wAHCBQoYYLBgxMkDByIQICAABAjSpxIMYBDABgzatzIEaNDAhVDVvwosmTEix1TdiRpMuRCCQliyowZQeHChg9bikSpsqdHASB1hmQplCJPnyqJFoW4sMPMpycWDsC51KgApD6VVrUIdKvEo1g5ai06cMLTszYFUvUKEWxYjWOXxi3q9u3PoFsHtjj7tMNNh2zbXrUrtmvguULr2kVsUqAIvmcnMAR8eDBhuIbZMm6p+O1mkQMkRID8tMXknJotX74bmCter53DfnZ5gvRZEWopp16N+fXW2TtVrwZOUYUFh8gdVtCANjdq2MIvEx+ZGTrvjNMlHk+evELf061jY//NPpH81+iEzQcwzl0585lp1+6+DkC9YN9VxSNVP7C2bdzgVUZffdX9VmB+6C12oFADiUZaTX89Z+CA9rkWXoKeLajTQo9BJlmEF1KoYWIj6qRfViWWJFUHEbToYgR+SSWfdfRVaCOGsqUImlQ89jiVbjRedyN+cuE4no4u+aikcyHWiGRwRNJl5H5PUrTkkjNO6GSUJHJp4pQoeqnilT5miaCIYpp0I5psKeDmm3DGGaeZRRpwnQEOIbBAZXpWZidveArQp4HtFWrooYgmquiijDbq6KOQRirppJRWaumllhKg6aacdsrpAYEi4Omoo+L052qBGnAAqaxuauqqrbL/auqdDqkaK6leLXCAQ3vmemJPDh3Q61YLBDtsVcWCCawAwrZWFQFAailkldSlWdKvSVHrbADQSnjmlhdaGxyb2xbVbZPTijuUtuUpm6265Z4rILiVwWsVueXqJO986YaLLm8VbrVvkACze5697eKbr0kDS1swwgf/O5zBgTX8bb/1SiwdxWxZXKTCBkJ8krspBfxstBc/7O+8GC/MMMof05uayIKB7HJFHktpc5E0c7XzzRLlTOLPXWqcHsdeCf0l0V/2jG3JSAsMs84yQ+c0yStFfbK3MbdstdEK9iw111R7HTLYGYq9Ndo5Xn0100ArzRnWhbnN9pFqLyW3mnRv/zTk3VTmLdQCdEp56mWhHovgoNAdTlji0GEq+eSUV2755ZhnrjnlgNbauQCO25UqraCTHvpbow94tJMits7666orCHvBrk9be+w5zj7x7bvrjjuKvq8evOzD/5518bkjfyTvxmerPJXMC29789BPbz3tzwNPvfbYX9+79xtvz/333ZdP/vni+903duuzHz3x4Kfvfvzwmx9+9s0/Xdj7GfKfvPz7o1//8Oc8Av5Of+rz3/IMeDwABtB+0oNg/dDnQARiRoHVE+D/HDg/CQ5QgwsE4fYs2EEPbpCCE+QgCX+CwfGh8IMcZKEIXXi/GS4rhhe0YQF1CLUWGi91iPPc5/9OFxYgBrF0QyzY5pbIxCY68YlQjCKkbjUqUOWJiqUSIqq0eEQiYsWIj6sVrLC4qVztSgCKK0qyfLhDEx6pWUALyd6u1T4Z8rCBZotj0KY2NAY+0I2B06Mc+bg0PybQkL0RJM4IOTc29hCR7BOcUOY4LkjaEZBhUiRFKLkuR+IRk8uSpL4YyTdP/vGFbdPkRDhJHVMe8o51U+UeydZHWL4SlO+SZURYeS9LEsiVidQlt0hJR2CWEJV4E+YwaVlIW+bQmZFUJi8T5ssVXnJiomzJNM9jzGvWUGXC3ObIuvnLampNb8SsJDS9GUFsSjOdnTQnOU2GTmY2Up7mzObL7Fn/Snyuk0D6LIk4a+ZPXEItoCEhHDyp40WkgFF0XAwjErcoqDR+SYoYzahGN8rRjnKHjJ8KFUg5NaskmpSiDfVJqsYIUjPyii1rLCgyqQRHWQ7UZzL9pjvDudBe/tOa5cyjIm8K1KLWMajg1CVRj2rUeZ7TXD2l5k+ZSlWEDpKfxcxpOzdm1UViVZ0G/eRMM8nTr8Zzqk7t6iajyk2tpvBoal0lW8fpVhjuVKlzJSha8/lOs7ayrifkal8BR8OtGjZsg2VZWE+pU8GWlbA3BGwyH6vYsUZ2qnGdJWTbaFnOOhavfvXpYm/Z2YMmll+jfWZqo0lZ1JZWrI2FqzAVGtp2/6W0Jw9FXUQhOtEjMq5IHg2ucIdL3OIuaqSasqKgkKupkqLUdNAdIkvJ6FI0wrSpkqWpRfW4VKem9bQEey1jD5s28DpMvKSNLWJbG1712tW9qWTveeEbwtWyRr4pQ69q9cta0G72kXsVqiC7m93Crte/laVvBu0LUPPmV8EGfq9s8ds1BmNXwNzNK04DbOGnTlLDF+4w3G5GYA7z974Idi2EL9vhzO4SxFT1btVUWWIR85XCZbMxZh1c4RMj1cf0hKoAHMAWB+xWt72V6G1Vktsiqse4UI6ylKf8xJMqObrPtfKVV3OALnv5y2D+sp2CFeYymznEQI6xqszM5gNUd/+7LYmpiVecrZrSGMYy3jGOa6njFvM4x2n+7p6b2ec0uxgiNQ70jVPcXvIG1tGTZfR8Ib1gQ/+Zz4rWs6QfTOkIP/rANsVzgVl811DXtq1z7nQoL03oTPt50PdM9VtBfedT01XWEqa1JhNNZwC/etM97jVsVZ1LWPcT158ur7Gziuz6lrrW/x32rHOtbNAS2StGTjJvl5ySJn/xyE6mGJXHTe5ym9tRWkYytzvibYeC+9vaVjdvGkDvetv73vZmJ7WTzW9nh2/NbS4zmXzUAGuiWdjjVZCdyTJwHuFk1J4ltmm90nCHGzzGEJ94XioeoYxLe9+RrgrHO97sSiNcfSL/G/lpPJ5wkAeS4ir/UR0PLvGPx3fjKn94yT3t7wnjfOQ6L/TJ9/tZkcc86K5OetGXEnOZszy9NY8lzHN+8Tz/2uhUnznGd77qqQO96k8nus+x/nWtW93SXuc40od+TLbru9pkVzvYuR5xXTPo6HMXetRRzpamr33vYp92v1/+c7mbPextH/tS5MR4xtvg3SqFfOTjHe51c6TdkxfZuTfP+c5TOd2Vx3IXRb/lywTczGNm1unZTHPB91x2AF+9l9983a3rXeFwjpuo6e7rZ+9697d3OVmBDWi3/9j4Qf6wrfUa/MEP39TRbrnzSb303y9/w81/PdyhnWDAJ971hId+//fBz3OTK9760Yc6+al//qECX+neR7H4VRz/49c/+aO8fuuFz367D/j9yGd7vud++ieAAThiLsNr92eA7fd/BXh2AXhoy5R+gcd/dbd96Dd+Fth7EchqsZZ95ud/GTZkRSZ5uGWCTIaC3aaC7PZknveCMBiDGAV68GZ5G4F5J0h5NUh6byF7YJZ6Plhm+zd9F5g7seeDtOcrDLh+RfhGuUdiALiAglYRISAAIZCAUciEHIgRH4ABXviFGDAD0hdyEYECF3AcFnABasgB26KAWjhsMgAZGzCG4QcRDxATyCETN9CGWbiBw7YBpPEB6rd9CyATeSgTD+AsbuiHjPUBtv8xhxV4cxBxAYboEE/xhPtEgd/3XoBoGzEQiSHHATNxiDJxAa2xiETYezFgGzIBApsoiR4wipb4FGzYMX2Yip8EAqwoEynwipF2h7IoAGfhARVzi9qXPC6wAtvRHe/xFK5ofwcWi8HIFyVgiw+ITwVwKN7xFDjgi4QnijNRAQKwjTNhitaoiW+HjMq4jO5xFs8IjXAnjTGhActxFtV4jhqIi4zViaSRA6AYfiWwiyhwisYYgu/liKQBid74fAGAAqyoiAVZfoWVAoE4iJIIEbaRiAR5jak2A5DRixZJhgEAjMPIhxx5ezKQASq5khnwjgvZdRPxADI5kzJZixuJjvDdqI8hWYcZSH9vSIcSCZPz12g/uZNBWWzWVoI66G5LOXk2qBE4mILiJoNUWZVWyTlZNno0uINbyZRPiRFB6GVAGJZfNoTHeJbQc4Syl4TEYpYGqZOYsXAEiJNueZQaN5STVpT/aJdZI4GoiJZ8CZRCyX0+yYhG2X8YOJf5CJiICZf9hZecppcv2YQ8qZiF6ZjpyJhICZnBJoWLxpnF55maBpqYdoCfSZhEaZh72ZgiaZmpiZk5qZl3iZp5qZqTuYUiGEd/+ZayaXOt6YB0uYS2KX+0GZnDGZu8WZk6ERAAIfkEAAoAAAAsmACzAMUAJgGF/v7+RzyKAAAAAICA0M3cOzs76ejp29nnyNnbAHp6/gAA/osASkCJ0eXlQjZ+jltUjSVVyRoaLGhoq2c+/8B1/9CXKxgAshg5DhUSLAAA/+jNZTJzaUtxjjc3yXQnMylaTldXqwAAql0A/5QTeStkb0dHomRFwxMs/6M1/8qK/9uyAFVVKCgoph1CuCIiqqqq/50l/7hjXEV7u9LS1Q0f0w4h3H0Z/7BR/+G9AAAAAAAAAAAAAAAAAAAAAAAAAAAACP8AEwwYSDCBhIMIJQgkOBCBAAEBIkqcSLFigIcAMmrcyLFjxocELIq0CHKkSYkYPar0WPKkyZYuRS4sqKCmzZoRZjZ8GHNkypVAPwoI2ZPk0KJGgyqFiRTl0aYTdQ7ocLNqCYYDHEKE6lRpUKZQwTb96ZXlU64Xz3LVKaGqW51a0UYkW5ajWKR3i9KtqzFvT78xdbpwW7UDw7hy9/IFANhl45OK+T5+qRbqTBCE3UogiBht5LqTfVYeK2Bxx9AiUVtcmCBC5qouOPNMXNr0RtUVcVP8XFb3RN8SF5Z47RbEzq2ea9sWSjR5c668vQKfOxqpwBUYHmp/mCHE2+Ny0y7/71tdb/m/ypdPT/vc8oDs27dnKCwbOfT0ttfrx296f/um18EXX3dVzdTZfeMxF95+CTJ2Xkzr6TQcccbVtyB/i/l3YYMayqVTa6/ldNhsyXH4oGMnQoahZClS9p91WGGW2WYj2hfWiqC1KNqL5pnII3o/BobVVBEUaWQEhmF14I0+Xhgkik3S9uRJUg1ppYW0Renchgl2iFaVV1q5JGla3jclZWXeeOZIYIapJIkIdqljanMmJeeadOK5mpt8jokXjr3VmZuguwEqHaG/IRpcAow26uijkPppngHjGfAQAgzQhiltlC5nqQCbJhfqffGVauqpqKaq6qqsturqq7DG/yrrrLTWauutuOaq660IEODrr8AGG+wBn/Yq7LHCatWpbZ8acACy0P6q7LPRQqtspQ85W6212VK7bbKZcsXAAQ+FCxUD0S0lwAHmNoXuuu0i9a6J7KL1br3ihkcAnEze6SSX43kZHlf72kimv1ICrJ6i1Ok5MEUFK5wfw+xJ3B/F6+nL78EBY+xxmqQ5/LBEEWeJ8JYmdyxyoiuPXHKJJ5tpcYYet/zwy3Gq/G/KC9tc8chQ4dyvzgnzPLHPGcslNMc97wwz0SgDjdTSf4L8J9KGqou1zxobXHXMas7M4tZSN0V1j2CHLHaOZJdd1NnoWW0e1nID6fbbG38NtcxGX//c9t0uwQ1h1l/VvHagfwNukuBQpn314YcmrrhIjKtY9+B0Oz735CdVjqbmdvdNs+ScQ5w32nuHLfrYTpdekeeiXY5i5qmr7XpFDEiK3rKmFRvvn6PeyPtivmv6u3m7Jq/88sw37/zz0EcvffLD89Wsp9liK0D1dV2P/fbac1+W98yaKKf556ffMfoNru/++wuzH7/67U9Mv/33Xyw//vXz7///+ssfzfbXvxwJkEUEHOABDVjAAM4PfgBUIAQd2EAGPvCCEUTgAgNVQQlm0IIYpOAHOdhBEIrwhB4c4aESWEKVpAsoL4Qh4WTIQhOisIVm2eAKdaiuGpIQh4XjYRD/hUhDIq4khi1EYg6N6MIZHtGJTfQhEIXCxCVOMIU3tOEU7QLF03TRi1LsYRXBuMUnjpGLYRziFTVYRiuqUIxr1CIbQ9hGKsbxh3Sc4xvVWMfbfNGPaSziHXc4yCkqkYyF5OMeBZnHNh4SjWcEZCTJE8iViM8r5CNe9r53SaVkUpPg42T4tDe9UprylKhMpSpXycr4GOtbwyoWLJO1SWbVsne3BGUng9Isb83SV9P6JbCCJy9yCeB4MZnXJO2YSBni61wPeaa7ugY5ODYSj0dr3e1Idrq4LdNBlWwi6bYJOzqF042LfOI4b1dOOzXTjN8U2DYj0s5BnROR14ycNudZ/89C3ROS7xTnPsnZzcH9U5IBNcs6XdfPRB2UkvE03Dwn0tCuRPShCiraROlZ0MYlFJ/pFKhGN1pR6mAUnBFdaOlKKp6LpnSg7Oyo5Vz6US6qlHMsfSRA82nNbI50ojn9I0J5qki/wZShMv1cTYcaUoUedaVJjR1NiSrDm04uqCfVKVON+lN+RtWcU22qF62qOKyGNYuIe+rkcvdVo+wSKJ+0Xi7lGkpbggqZgyPmn1rJ17769a+ADezzXilMYl1KmNKaa/cUOz7GYtKxnuwWYoGZrQYg4LKYzaxmN4vXk4yrXPbSKkSXOtqFSVNeD2kAn9yUAGquTo6wxSZXRbXaMP+1VmlttedZsTi6CyGgtle6LVrMSlpmUlWdvgWulYRLsNz6c7d6nO19fqtcrDA3aM51KHRjq09NVde6rn3acaP4Uu9+tyDhzdl40YnW7tL2vAO5rtmya9HiotS++6EufOU7NfqadLuy7a1595veoa0XpO3VWnLhOwD+4s1rqDvwTsVq0wUTGLcQ9iZ+hVpan773wsP1b0s3nFWP6fe8Du4JcSW8Vd6ybsAoLjDTKNzi6Ar4wzHGcDWL6mLuKhjG301xTFZM4w4nuHBYO3GQZaw3Fhu5x2kFcnWFHDgRi9a4RcaydG+k5CkzOcJZvq+TM4rjJdtLd4N7qyUhy0s2w9X/zWu+q6YgRec6J0CweM6znvfMZ74S9peGBdVkCXAtURrarmpWSS8HHczJ6rUnnz1maDmsZSgT0rSdNcm9Mj0STi/OypQWc5iv7CCyAo7IR2bkqCW60QCg2tI9TTVyu0rQDBsUwJf2MN9a/Wob+9rHSFbrVUFd4hKb+m69BraqZS3SqJGU2LiO9Y13/Wxbe3TMoma2U2kdU2vPlMTldTZQoQ3uDR/bbckO8K/VzTZhl5Xc2Ca1vFld7R0vG9Y8frG4veptpcY71PM+d9nSnWttI3jaquM1vFdtbHefeuEGn3DE/ShwqRFc2usuOMJthxYHYNjjXHEAnBU9co/EdbF1/8Ulw0S+8j67/OUwj7nMWZVok5ecIydvbMp1OcpDq7zmHXHWAYZO9KIb3eiUiubRl870eRf7fEJnutSHjpGoT53pnrZIpLOOO6dHu3CnLcqm7fVlDf+74dxGar+lWu5/VxxoF8+3suFZu8e12tUQx/e9M/7jfdfa3nQ/O+gwd3e8rx2sbV/1212Wd77Lnd1R9nu3AU/exG+Z4/V+LeQ17viqOhzZjZ975d3+eXSHfvMYF/22Ja92yrNX74HXdcIzL16Gh5va4z68OwVv7tIP/PScV/3B9Y17frt++MKX+MbtrnDd69bysG928f+u+eCj/vHunf7kq5/66++93WmPCf/ImyuA8TeF5UDH+c3Vv3O6pn8jn7IZy+U/8/rb//745+v7NZLzyLYf5fuXEf3nfwEIAAPYZv83Pg2wgAzYgA74gJWWfDUmgU+GQFZ3dUdXdRgodW1SWw1wZV4HfSwSdpAWTVxHEQzQgaulFV/3fd43a6wnJAzGgiJIgREIfjHoEirYJyAIcD6IdjlIJQyWFT34dL0Xfjo4hDTIe0woe5jnHjNYhC0Ye5fHfGuhhFJYgy8ofbMHhfC1hLanhdnXhQCChZQWgk1YhZvjIWY4haOneL5HETvoJmA4cRVog9m2fGv4JW0oht1nfWP4hGUYhWf4g264etrXE3MYJnUYfa//13lUqIehw4eEeIjIt4WISIYwUol+iH1/2HeJKINfmIVp6IhjFYdR0YelCIlvqIaTeIWcuIp4GHCouCh2dosJMAPrx3+7yIsJ+Fi/SIDaQ38tl3/GeIzImIy2UoAHGGfM2IsCCI0GKI3N6IyVsoFMl3Trgo1Lh4ZhGD8XyI3GZIDiaHQnOBFbN2lG+G8kmEwmSHY6xn2eOI8uGIhWmHvHp3ys+Ig4GIo4BXyfSI+RSHyauH21Z4c3iImniITD5nzP1Yn1CIoF2XryGJEC2YqSSHh3F3cW2ZED2Y8TCVUOqV0Q+ZGR548NmY8TqJD6CJKCSH0HaYotyZIUV4s3A5AX/8mPNEkei3eTI1lfsriTZIaS7/aT/1WSGEmQL2mQ6vWNcMiQRamSdyiUeaiU92h8FWmSgBiQngeVD2eUIxaUWxlsXgl6YOmNCFmVLnmVMNmUaYmWGTk74WF+81V+HUeNeBmMCPiMxUiMyviXgBmYf8mXAViNJKeXb4aY1uhzPOcp5Vh02viYRQeX+3iJKxSO3KiBktmOLpGO4kKZs+iDnOlZ75gv8RiTlTmTY9mVQRiVWZmUKUABsjmbFKAClsmVMDgRJjABvNmbE8ABg8I1p+mWvKUCC3CcyHmcI3CbObmQE/EAyZmcNhCchceRkQgD0ZmcFKCauMmFDJCdyfkAhf8inCF2loa4ESkAnsmpASu5mrkZABOgnsjZLklTnlKpZSMgn8d5A+3ZnYjIAfp5nBPAMtWJk6oWAwF6nDgwle4pfR6QoAsAnA1ToOYpPxoAoQuAAgzqn84JnRDqAU5BnuR3nyj1AiwgINthASKQneypllQJEw+anCJgAaZiAR8woRtpoPBUAKliAdGpoQnZoKsHoMlJo6iCo81HoilhohiAog+hotHZoi4qpM4ZADF6nDN6Kjf6M0n6muyVn/oZA/3ZnBUmETKQoCZAoDlaoflTAfq5nGPqkVxoAgE6nhSqpBiCAvKZAtxJpjVJEfopnmrapaipRRcKnjDAnHKaiQH/4KHZCaJ2uqZ4qhIqUAGWeqkVIKUb6qc8aREP8Kmg+qkSOqi0R5ypGadaKZFLSZGF+qLn2TRlaXpsipQ6aY97GHIfd5eKeZiE2XN2RYx+KZjCOqzE2me9imi++nPJ2pjI6pibGZmbOY6WiEaYiY2aKZnX+pjnKBGeCU2vumqjqWml6a3h2mlld2u0WqUU8QECsKV5YpMDY51JGZov0gIXoB0XkK8kQJ2x+nuzKpaqCgE1oR03wa+t+ZWTensRYRMEaxMQEKkHa5YJe4QScQEM+xBVYX7yhJWtSqV9ypoBsAE30bA2cQGkGpL/+K9OKV0nMLIYWxUbEKLweq7XtrI3/0YCVUGyNkEDMtuvFqej8+qqzdGyLisAhBGzXEqUCOulipqqZBmyVZEBAjAfVXECPRuxsjqxpFexNxECBJKxV6u0Esu0H+u0IBsAxFETLXCyqyqSWvuUEiGwxAGxYpu1ZIuqQRuIxPGwbMuWTGlgNquUOEsYNWCwdeuvb/uW1QEBjNu4jIu0fXur+Hi3m7qo6tq2cimplBukHFqmWGu4k9uxnYu3jOq3rwgVdNlfdhlyeXmsvyoX8we708FyxVq7tnu7q+S6zKqsjOl+y+q7noJRoOmq69hHnGu5ZZu3o1u5ZptEhVi8intSwvut0TutBUSKgRua0rtMw+uxpNu0ypELRNhbvemavB00vjL5veYLvrVqSM9rvcwbvsh7ve9bvup7v/F7vvULsJx6vPPbP+h7qvnLvgS8vg0UwNoLv/7rvAo8pcu7wPLLwPY7wAaMvxCsvw3cvQ/swOK7v9lLvN80vdCbvhRswe2DwCA8wRdcQSjsvSX8witcwQDsweTLv/8rgL+rc7q7uzwMvM3KLAEBACH5BAAKAAAALH8AsADeABABhf7+/kc8igAAAACAgNDN3Ds7O+no6dvZ58jZ2wB6ev4AAP6LAEpAidHl5UM2fpEjUpBcU8kaGi9nZ6tnPSsYAq0aPP/Adf/QlywAAGYyc/+VFv/bsf/ozggUEjIpWow4OKsAAKpdAMh0KFxFelNVVXgsZW5HR2dKcsMTLP+jNP/KjABVVXtSY6QdQ6UsLKdmQqqqqtV5Hv+8bSgoKJ1hSrvS0tcNHtIOIuJ/Ff+dJf+rRv+0Wv/gvAAAAAAAAAAAAAj/ABEIHEiwoMEGBgQIMACgocOHECMmFICAgMWLGDNmRKCQYcSPHycaOKCxZEmOC0maXGkRpUeQMBuKVMnSpEuaNTUiSDCgp8+fQIM2UBizaEOFDAIoXcq0aVMGRI3GVHggqdOrTqEKqIq161KtUqduteoVq1auZc3yDMqWLcqwMBWm9RoVbkSFBOZ2xasXa127D/n2dSp4MNO1PxNIWMxYAmKfbwFDlGuY6V/JAApXDqC58mXJnQ2HHvx4QAIFqFOjjlA6MuajAjYv/Qx4dF/bemnbxT2Xd9rSH1QLNwHU9WvKsnXD9V2WOV0Brx063ysgr+wAj08LF/7YOGbkm5WH/53ut/p1ztCjZzZ/nfzVxy62C3fx0zvo2O3TR3dPmH1y/cf5F56AlSEmgXzbSQCZeFKB5xmA3xEomoSDMWgUf01heFhPCUSAoHCs9WRfbfj9p9561v2X4oAnajgbhX2tRcKH25EgooVFOThhizDm1mNvEN634oNDGsbTCh0opKRCGICQ4I1BkngejmIVWeGPaVEZF5bNcekVT0kuuSQGwn2wYJS7lciiei4q1SZ6PFp5m5ddHRmmmE1ydyaPU6K5HJ3lyeljnH0KOtdjJtCoAHF7sqnmg4TmZ2iWfo4H6FVvZuchgiE2ut+jOzo6aZejPhepiueVNiOCCtanJUg6Xv956oClUjcrkakG9UEEvPYagZnFvfpRrHPeOmGt5Rl7JbLvteWsq5U2CKqsohbap7JzMutUac8GNWKa11Yrabifapvhpdt2++y3f5IboLmWoXsutj7CuxS36noaoLsR2uumvPHSC6S/2CVg8MEIJ6ywS9FN1OdLkk2EAFkPTpwfxIBJTPGEFv8n5scghyzyyCSXbPLJKKes8sost+zyyzDHLPPMNNdss8oY2yVxTiYdsDPPOnXUcEc4Ad0S0UZvJPRrMyWN0U1OX4QAjxv3xcABSMkGlqNoVXZW1Xpt/WnXhn2tNZ/XEUDsoOKimp/AlBKMtmxqT1ts27TyK6S1183/vVndepNI8JvC3gXwi3I7eh7gb+ONa+Pl8i2b35UxbqLjxwae5uCHK0W5YZavGfm4kL8r+WafDxY6pJgvq/mfnCf+6eJrAwk3qa9bGvt5qfe1eqiju3158Hn3rXjatVN6+3OcL0+d7PsibzfbxD8+vOmkT3483cl36XyguUu7u/GzSx/+hZ2j13zr2fK+/d/dm8p+vefnmP6bvev1O7XVZ156v6erTP7msr+79c91/9tb9lD3vsrFz1bzG1j9qhRAwwwwLQWkHvaEJ7oNFk975ePe9GwXwbhNcEvjA2H0RHhCWN3vhd/DVPpyBheHaQ0lDxsaRcDmo44NiIZh0Vh+/3z4oJsZ8YhITKISl8jEJjrxiVBMGRClIhIdTrEoVWTa0jCTRS5uMWJfzFgYdcajMn7KjGd0FBoDdKI1RkiNcEyjHNkYxza+sY73cWMe8bjHOdqRRHpMUyAFyUdAFvKP0hqkpRS5yEP+iZGI3BIk7TdJSjqykX6MZCIvib5KisWTkuSkJg0HymFFq5OitGQmDTlKTNLxlbC84yoJOctWmrKUpEzlJ3UZylracjKnVKUsY9lHYrLSmL/MpS9RuUxmIvORvGxl4YAZzVtWU5nPdGUyXYhLal7Tm80U5jC3ac1w9jKbmzTnOcdJTnCiU5zFZOcx5UnLdmKTntqMpz7nuf/PetozMMHc5Tvhyc+C+rOf9pwmQL+5UIZKJ6DrRGg7FfpQh8Kmm+7EZz7/uR6MVtSiHQUpRS+qniticYw1RCkVVbrShVjxpVp0aUxNGpOJRPGmOM2pTnfK0576tGY0hcnPouYzhVQkagRg2Exh6kWZNnUkSE0q0pA6NUfxMC1Xy9pmxDZQgXZ1S2QbjNm2esGyZJCE6uSmSF8IvQjRboTKW6tHH5pCBoYQfnD1nlzXWlcBNhB0D0zWXtNqyr5a8K+qC6wM50pSwhrOsIMpq1fOGlfHZlSi4qtgZBHrO8X2h7EhtSxAIdsXyXaFsnoV7UdVCxvS6sW0WEGt/Fgb2q//utC1c4HtVWQLQdqOtLYAXKBf7+rAvM7WtuVE7mM1W1rO6s+z8xqscoGJ27To1im8FaxvIarWA7aPfCvEawuTq9F0etB6dg1vccd7T8w6M7gcHK56AWvc3k63odutblmu25TsLla65UWffr3CX6b497MAdq/9BtyVAi/lwNHdLmgJlz4HKwXCAUuwQWHHXL0EFSQ23CoOL8ZUMDrVxB8OiVGvSikiTuinMI6xjGdM4xrb+GMplohRo1pUikRVqU8tsRhPPGSoUrUjDTCIkpdMNa1hTQAs9gpXA0zQg5IorFZTSAPy1ZYEWDgAGH7RhLlLXgX+BwFcZouXnUtA6GZY/8J87ROa0wyUNROXvuy97IY3ujk507nOXw7zv8Y8Zs7N+c8cCrSbxaxhK/d5iIj2iZ3nm9j6ave+q8V0a/0cadMo2tL/hXN+OR3pSbvVfAncs3mpTEFId9rU93lrnvGraeCaeUCHRjSspYTq67E6oqoWMKl1/elZZ/rX3T0vxzrtaTZjcNGDbjQ0vdtDZu8aXL3uILLLHOwFD/vP125Xtlkn6lpTONfgLnaqHT1tc78Q3XQOt6Vkve52b7u9gvt2vNXtawV79d7U1Xea5S0teve72//2NwoFzmWCo8/g2lZ4sgE+Wobny+H2gzi53V1oi6srATmGSIi9NuL/hPwhXf9EsZB1tuIhKuzlME/AjWdO85rb/OY4H9nJHTJUp/X4qFEDssqXSvSnFs1oUKNqk7f65Ch3ZcoS5za7LYXlsFHF6Wbhd8QRDuypZ1a4h71zpY3dWI6XEHfgPTUL681ne0d9uWDfrNg7C2oEl5vidO1wbp1tVmjDSdpt153ercv3yfr9t4gnM74fHd+wU5ruZLc11yd+a/TKV+3iZfuq365nxn8wvZhfr+bfy3law/fzl4/1uIFn9lHHvblzf27dI9x6dzMYK1rfON7LvnsUDX6/hT/t4RXfebdPvrC/J3DwYzt8Qsf59a9d/m6bD/jN5xv6e499m2f/5tr3nsJtVf3/2g/uddIfH+6Nl/vjZR/5xHcc+4TX/rO5z+i7lz7v8C/LzmWiEAdIz/+b4QAsZRQpV2Qrl1KdI4AJmHMM2IAO+IAQiET7BwAFyHJEhoATWIEYeIBBNIAndYE1xCMHMIIkWIImeIIMQRUnuIIs6H7V10kjwYIySIJEEYMzKINL5zVNpzUuaH9XhnVXMVZek3us933EZ3qV5z8qFHp4NnpVZnzl5235p3zy13f0F20+GIWtln6wt37b135HeGz3t2lT2GDSh13Ul4VQyGFliHtn2F9p6H1j6HtteBVEyD9yeH4BV4dOcYcGlIdauHB82BR+qEFG+H5cGH1VaHhX+Hdq/xh4X5eI2eeF8weGzkdtEpR24pd55LeGkGh+nmd5jseEY+eECaeHSHh9khh/lGiFlviCUriKwLeIwteIPQiIbCiLVNiKjPiKjxiJqDeKmyh6nfiJT2iMWxiM6keKkGeKXeeJ1heKSgh6w9iExRiN0AiKuaiMfZGB/fd/sqGA3giCHUiOLTWO/qKA6RiB7NiO7viO7TiO8lh0Qxdk9GiA90hGjtIA/NiP/viPAMl7qCiGAymQgGSDN7iCNZiQOOgo+MJlQxGGkheIlFeQ61F1cyGEZcMjD5kv7IKNyPiMIXlbyWeGn9KR6vKR2jiSUpeNsciNihggKNktKnmMIHmTK/8JjKK4jPcxk+syUrd4iGfHPO5zksxWk6dIkS3JkugHk5MYIT7pLEgpkjhpkzkpbIPIFBx5lEApkRPpkkm5jTvZhVDJlRIZlHOYeLdnhw5plrAYlky5h7pokjLplr94lVb5kmMZk2XZaVNZkUq5eGApiHPphkbpl115ibZXkoZZl4h5ll6JlhYJfkXpmJH2l0tZlXCpmYTplKzYl5eZmG9JlVhZmGx5mKEJmYpphGvZh235mKMJmINJklm5FFsJm3eZl5tZmp45i5aJaJgpmHGZioEpl73pFeMYc8p5MDXggULlnM9pjgQInSBGnSq2gNgJj9q5ndzZnTs1j/lYjuD/aY/kWY/mmTEiyJArmIJboZ4KGZnwSWgI6Z4juJD0WYI5WDY7uFWSWZzE+ScYiVVXdzZvaGBxKJSuZ5quSYvMZ4vxGZvIV5ueU6APdqBp+aCYaEKayGvjt3X+SZAfSoYKSogUemEWOpkYqmwItITVWIrXiJe7qZuduZdP2aLN+KIyKpvDiX8jqpUlCmYnGqJfuaMiepx0yYzs54w6yplLyps0+pk2mqQ4GqNUSpp6OY2px6Gc6KGzmZkwOqNYKoxaSoxcSqRDyqQR2qO2+aOC5oi4aKaUuaHY1qG6d6GIaKSNiaRfqKRemqN9moxP6pt6Wol8KpxoaqhOGqY8OabW/1imh1p8cMpWlTmorliokPqo/yl4EhoAhYhWb4qpPIqnVzGOAIhXpWoY4siB56iq04md6+idsBqrsjqrMDOe57mB4SmerPqBtoqeXHOfJMiewFqC/dmlpDSf9Gmfw6qswJqfYrWfXlOskRqgZaGRYjWgZMWmQWqsl5qoS+EBAuABRLmpnVpZEKECFpCu6moBG4CoftqUS9ECFaAkFVCvJdAfkPUCE7Cv/DoBJ7CgvFiLgcMDC1CwBluwGuCuVUqbTPEAqKEkqoGvFUQDB3uwMQCwlNqLqZYCFXuwMtCt72qcSpEaEJsaD3AufTUCHXuwEECiDDp9DhoRKrCyB8sBmf8KqkUaABVAsgohHKcap0sxATRrsBRTrqnlEBowtAWrAzf7pQwbABmgGiWbGhUQL3V1AkpbsC/goy+LhjH7EDKQtQXbrgYppJqBAlLbs8KRAYhzOjEgtgswAmvatXD4tQ3BAXC7ADkAotxacUpRAsIxtalxA22bPRCQtyIwtwHboOQCAxQAMhQQAh1LttIaiWibtgIgH2yrPpIjAhUbAo/7MRQgrpyqrXZbACJDARWbAnwbqSkStaqBAQJAJsKBAoUrPCxQsaEbMhNKtwZqtzAwA3eiJJFbsTZbtn0bqjubGiCQJz57u5/nuQYLuiFDukZ7XA2RtEq7A00bsn6rFA7/oCgK0AJWezpYm7Utq7gZK7AJtAFKm7Ddu7BpqhQOSyMoG0BCO7Q44LKLC7OBw7E0qwIgK7/wGgA0crLlK1wMoLQswL/ry7iphrcrC7/xa6WAGgCAKx82ILFgd7grm7gOHKV7Sn4bcAEmfMIXcLwV3KRXGgAP8MIw/MKbm8DpBwE2fMM2LLchzKgu6qhO+6dguqKiirEiTKhTasFI/LSBOq4sysM36sPe27o6qajfJRukCo4BaJ06Jp28qkOv+sXXoYC0OsZkXMZj3KsWuH8aqKu5uqpt3KoZmJ7DKqzDWp8piqBshKzuyaz36axWA61lU7k/rLBgBYRZga1DaLq+9/ip3lrF1OjEUgrFBDzASTy/S3ykRVypR8zCk7zClkzFfJnJGrvJQMzJlVzAQpylc7qldYqid3rJeSrK7EvKhGzKtvzJqSymq0ymrWy2d5yEubyou9yovZy8yOu6jHma/eu1i4zHpwfLyvzA/mupnlzLpYzLjqzK4kanRWinz6emvbvMddvM3pyhaNfEw9zDxRyprwzKNQrJIyzJp0zJt4zK2azL28zK3ezK3zzEO5zOT7zOOHumgyyy7gyl8GzE8lzP9HzN9kw/cprPvLzPvtzOwUyWsgzBtNzQ1tzRBn3RHqZDp0pfI90XqfrGXYzSNdUm6hjGAQEAIfkEAAoAAAAsgQAbANwApQGF/v7+RzyKAAAAAICA0M3cOzs76ejp29nnyNnbAHp6/gAA/osASkCI0eXlQjZ+j1tUrGg9lCJPL2dnaEpxyxkZyXQnLAAALBgAMChcox5FChMRZjJze1Jjjjc3W0V7qgAAq10Ap2ZBxhIq2HscU1VVbkdHsxg4AFVVci5qpSws3AsZnWFKu9LS4n8VAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA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",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
}
+ ],
+ "source": [
+ "import os\n",
+ "\n",
+ "from IPython.display import Image\n",
+ "\n",
+ "if os.path.exists(\"/content/rware.gif\"):\n",
+ " display(Image(filename=\"/content/rware.gif\"))\n",
+ "else:\n",
+ " display(Image(filename=\"./rware.gif\"))"
+ ]
+ }
+ ],
+ "metadata": {
+ "accelerator": "GPU",
+ "colab": {
+ "collapsed_sections": [
+ "JaIw_5YaUSAB",
+ "IFraNFqY6s7_",
+ "4idyWUhW68oS"
+ ],
+ "provenance": []
+ },
+ "kernelspec": {
+ "display_name": "Python 3",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.9.18"
},
- "nbformat": 4,
- "nbformat_minor": 0
+ "vscode": {
+ "interpreter": {
+ "hash": "31f2aee4e71d21fbe5cf8b01ff0e069b9275f58929596ceb00d14d90e3e16cd6"
+ }
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
}
diff --git a/mava/advanced_usage/ff_ippo_store_experience.py b/mava/advanced_usage/ff_ippo_store_experience.py
index 4bd94040c..60ac3b365 100644
--- a/mava/advanced_usage/ff_ippo_store_experience.py
+++ b/mava/advanced_usage/ff_ippo_store_experience.py
@@ -61,7 +61,6 @@ def get_learner_fn(
config: DictConfig,
) -> StoreExpLearnerFn[LearnerState]:
"""Get the learner function."""
-
# Get apply and update functions for actor and critic networks.
actor_apply_fn, critic_apply_fn = apply_fns
actor_update_fn, critic_update_fn = update_fns
@@ -75,6 +74,7 @@ def _update_step(learner_state: LearnerState, _: Any) -> Tuple[LearnerState, Tup
losses.
Args:
+ ----
learner_state (NamedTuple):
- params (Params): The current model parameters.
- opt_states (OptStates): The current optimizer states.
@@ -82,6 +82,7 @@ def _update_step(learner_state: LearnerState, _: Any) -> Tuple[LearnerState, Tup
- env_state (State): The environment state.
- last_timestep (TimeStep): The last timestep in the current trajectory.
_ (Any): The current metrics info.
+
"""
def _env_step(learner_state: LearnerState, _: Any) -> Tuple[LearnerState, PPOTransition]:
@@ -106,7 +107,13 @@ def _env_step(learner_state: LearnerState, _: Any) -> Tuple[LearnerState, PPOTra
info = timestep.extras["episode_metrics"]
transition = PPOTransition(
- done, action, value, timestep.reward, log_prob, last_timestep.observation, info
+ done,
+ action,
+ value,
+ timestep.reward,
+ log_prob,
+ last_timestep.observation,
+ info,
)
learner_state = LearnerState(params, opt_states, key, env_state, timestep)
@@ -155,7 +162,6 @@ def _update_epoch(update_state: Tuple, _: Any) -> Tuple:
def _update_minibatch(train_state: Tuple, batch_info: Tuple) -> Tuple:
"""Update the network for a single minibatch."""
-
# UNPACK TRAIN STATE AND BATCH INFO
params, opt_states = train_state
traj_batch, advantages, targets = batch_info
@@ -214,13 +220,19 @@ def _critic_loss_fn(
# CALCULATE ACTOR LOSS
actor_grad_fn = jax.value_and_grad(_actor_loss_fn, has_aux=True)
actor_loss_info, actor_grads = actor_grad_fn(
- params.actor_params, opt_states.actor_opt_state, traj_batch, advantages
+ params.actor_params,
+ opt_states.actor_opt_state,
+ traj_batch,
+ advantages,
)
# CALCULATE CRITIC LOSS
critic_grad_fn = jax.value_and_grad(_critic_loss_fn, has_aux=True)
critic_loss_info, critic_grads = critic_grad_fn(
- params.critic_params, opt_states.critic_opt_state, traj_batch, targets
+ params.critic_params,
+ opt_states.critic_opt_state,
+ traj_batch,
+ targets,
)
# Compute the parallel mean (pmean) over the batch.
@@ -285,7 +297,7 @@ def _critic_loss_fn(
lambda x: jnp.take(x, permutation, axis=0), batch
)
minibatches = jax.tree_util.tree_map(
- lambda x: jnp.reshape(x, [config.system.num_minibatches, -1] + list(x.shape[1:])),
+ lambda x: jnp.reshape(x, (config.system.num_minibatches, -1, *x.shape[1:])),
shuffled_batch,
)
@@ -319,14 +331,15 @@ def learner_fn(
updates. The `_update_step` function is vectorized over a batch of inputs.
Args:
+ ----
learner_state (NamedTuple):
- params (Params): The initial model parameters.
- opt_states (OptStates): The initial optimizer state.
- key (chex.PRNGKey): The random number generator state.
- env_state (LogEnvState): The environment state.
- timesteps (TimeStep): The initial timestep in the initial trajectory.
- """
+ """
batched_update_step = jax.vmap(_update_step, in_axes=(0, None), axis_name="batch")
learner_state, (episode_info, loss_info, traj_batch) = jax.lax.scan(
@@ -400,7 +413,10 @@ def learner_setup(
input_params=Params(actor_params, critic_params)
)
# Update the params
- actor_params, critic_params = restored_params.actor_params, restored_params.critic_params
+ actor_params, critic_params = (
+ restored_params.actor_params,
+ restored_params.critic_params,
+ )
# Pack apply and update functions.
apply_fns = (actor_network.apply, critic_network.apply)
@@ -412,7 +428,7 @@ def learner_setup(
# Broadcast params and optimiser state to cores and batch.
broadcast = lambda x: jnp.broadcast_to(
- x, (n_devices, config.system.update_batch_size) + x.shape
+ x, (n_devices, config.system.update_batch_size, *x.shape)
)
actor_params = jax.tree_map(broadcast, actor_params)
@@ -450,7 +466,7 @@ def learner_setup(
# TODO: fix cognitive complexity
-def run_experiment(_config: DictConfig) -> None: # noqa: CCR001
+def run_experiment(_config: DictConfig) -> None:
"""Runs experiment."""
# Logger setup
config = copy.deepcopy(_config)
@@ -547,7 +563,6 @@ def run_experiment(_config: DictConfig) -> None: # noqa: CCR001
@jax.jit
def _reshape_experience(experience: Dict[str, chex.Array]) -> Dict[str, chex.Array]:
"""Reshape experience to match buffer."""
-
# Swap the T and NE axes (D, NU, UB, T, NE, ...) -> (D, NU, UB, NE, T, ...)
experience: Dict[str, chex.Array] = jax.tree_map(lambda x: x.swapaxes(3, 4), experience)
# Merge 4 leading dimensions into 1. (D, NU, UB, NE, T ...) -> (D * NU * UB * NE, T, ...)
diff --git a/mava/distributions.py b/mava/distributions.py
index 490c785ce..d4869a4f9 100644
--- a/mava/distributions.py
+++ b/mava/distributions.py
@@ -22,8 +22,7 @@
class TanhTransformedDistribution(tfd.TransformedDistribution):
- """
- A distribution transformed using the `tanh` function.
+ """A distribution transformed using the `tanh` function.
This transformation was adapted to acme's implementation.
For details, please see: http://tinyurl.com/2x5xea57
@@ -35,14 +34,15 @@ def __init__(
threshold: float = 0.999,
validate_args: bool = False,
) -> None:
- """
- Initialises the TanhTransformedDistribution.
+ """Initialises the TanhTransformedDistribution.
Args:
+ ----
distribution: The base distribution to be transformed.
bijector: The bijective transformation applied to the distribution.
threshold: Clipping value for the action when computing the log_prob.
validate_args: Whether to validate input with respect to distribution parameters.
+
"""
super().__init__(
distribution=distribution, bijector=tfb.Tanh(), validate_args=validate_args
@@ -64,7 +64,6 @@ def __init__(
def log_prob(self, event: chex.Array) -> chex.Array:
"""Computes the log probability of the event under the transformed distribution."""
-
# Without this clip, there would be NaNs in the internal tf.where.
event = jnp.clip(event, -self._threshold, self._threshold)
# The inverse image of {threshold} is the interval [atanh(threshold), inf]
@@ -93,8 +92,7 @@ def _parameter_properties(cls, dtype: Optional[Any], num_classes: Any = None) ->
class MaskedEpsGreedyDistribution(tfd.Categorical):
- """
- Computes an epsilon-greedy distribution for each action choice. There are two
+ """Computes an epsilon-greedy distribution for each action choice. There are two
components in the distribution:
1. A uniform component, where every action that is NOT masked out gets an even weighting.
@@ -146,8 +144,7 @@ def _parameter_properties(cls, dtype: Optional[Any], num_classes: Any = None) ->
class IdentityTransformation(tfd.TransformedDistribution):
- """
- A distribution transformed using the `Identity()` bijector.
+ """A distribution transformed using the `Identity()` bijector.
We transform this distribution with the `Identity()` bijector to enable us to call
`pi.entropy(seed)` and keep the API identical to the TanhTransformedDistribution.
diff --git a/mava/evaluator.py b/mava/evaluator.py
index 201544338..77c8f9fa8 100644
--- a/mava/evaluator.py
+++ b/mava/evaluator.py
@@ -42,6 +42,7 @@ def get_ff_evaluator_fn(
"""Get the evaluator function for feedforward networks.
Args:
+ ----
env (Environment): An evironment instance for evaluation.
apply_fn (callable): Network forward pass method.
config (dict): Experiment configuration.
@@ -51,6 +52,7 @@ def get_ff_evaluator_fn(
of training by rolling out the policy which obtained the greatest evaluation
performance during training for 10 times more episodes than were used at a
single evaluation step.
+
"""
def eval_one_episode(params: FrozenDict, init_eval_state: EvalState) -> Dict:
@@ -65,7 +67,8 @@ def _env_step(eval_state: EvalState) -> EvalState:
key, policy_key = jax.random.split(key)
# Add a batch dimension to the observation.
pi = apply_fn(
- params, jax.tree_map(lambda x: x[jnp.newaxis, ...], last_timestep.observation)
+ params,
+ jax.tree_map(lambda x: x[jnp.newaxis, ...], last_timestep.observation),
)
if config.arch.evaluation_greedy:
@@ -106,7 +109,6 @@ def not_done(carry: Tuple) -> bool:
def evaluator_fn(trained_params: FrozenDict, key: chex.PRNGKey) -> ExperimentOutput[EvalState]:
"""Evaluator function."""
-
# Initialise environment states and timesteps.
n_devices = len(jax.devices())
@@ -226,7 +228,6 @@ def evaluator_fn(
trained_params: FrozenDict, key: chex.PRNGKey
) -> ExperimentOutput[RNNEvalState]:
"""Evaluator function."""
-
# Initialise environment states and timesteps.
n_devices = len(jax.devices())
@@ -292,6 +293,7 @@ def make_eval_fns(
"""Initialize evaluator functions for reinforcement learning.
Args:
+ ----
eval_env (Environment): The environment used for evaluation.
network_apply_fn (Union[ActorApply,RecActorApply]): Creates a policy to sample.
config (DictConfig): The configuration settings for the evaluation.
@@ -300,11 +302,14 @@ def make_eval_fns(
Required if `use_recurrent_net` is True. Defaults to None.
Returns:
+ -------
Tuple[EvalFn, EvalFn]: A tuple of two evaluation functions:
one for use during training and one for absolute metrics.
Raises:
+ ------
AssertionError: If `use_recurrent_net` is True but `scanned_rnn` is not provided.
+
"""
# Check if win rate is required for evaluation.
log_win_rate = config.env.log_win_rate
@@ -328,10 +333,17 @@ def make_eval_fns(
)
else:
evaluator = get_ff_evaluator_fn(
- eval_env, network_apply_fn, config, log_win_rate # type: ignore
+ eval_env,
+ network_apply_fn, # type: ignore
+ config,
+ log_win_rate, # type: ignore
)
absolute_metric_evaluator = get_ff_evaluator_fn(
- eval_env, network_apply_fn, config, log_win_rate, 10 # type: ignore
+ eval_env,
+ network_apply_fn, # type: ignore
+ config,
+ log_win_rate,
+ 10, # type: ignore
)
evaluator = jax.pmap(evaluator, axis_name="device")
diff --git a/mava/networks.py b/mava/networks.py
index 8302790bb..6be69d50e 100644
--- a/mava/networks.py
+++ b/mava/networks.py
@@ -96,18 +96,20 @@ def __call__(
"""Action selection for distrete action space environments.
Args:
+ ----
obs_embedding: Observation embedding from network torso.
observation: Observation object containing `agents_view`, `action_mask` and
`step_count`.
Returns:
+ -------
A transformed tfd.categorical distribution on the action space for action sampling.
NOTE: We pass both the observation embedding and the observation object to the action head
since the observation object contains the action mask and other potentially useful
information.
- """
+ """
actor_logits = nn.Dense(self.action_dim, kernel_init=orthogonal(0.01))(obs_embedding)
masked_logits = jnp.where(
@@ -144,11 +146,14 @@ def __call__(self, obs_embedding: chex.Array, observation: Observation) -> tfd.I
"""Action selection for continuous action space environments.
Args:
+ ----
obs_embedding (chex.Array): Observation embedding.
observation (Observation): Observation object.
Returns:
+ -------
tfd.Independent: Independent transformed distribution.
+
"""
loc = self.mean(obs_embedding)
@@ -172,7 +177,6 @@ class FeedForwardActor(nn.Module):
@nn.compact
def __call__(self, observation: Observation) -> tfd.Distribution:
"""Forward pass."""
-
obs_embedding = self.torso(observation.agents_view)
return self.action_head(obs_embedding, observation)
@@ -212,7 +216,9 @@ def setup(self) -> None:
self.critic = nn.Dense(1, kernel_init=orthogonal(1.0))
def __call__(
- self, observation: Union[Observation, ObservationGlobalState], action: chex.Array
+ self,
+ observation: Union[Observation, ObservationGlobalState],
+ action: chex.Array,
) -> chex.Array:
if self.centralised_critic:
if not isinstance(observation, ObservationGlobalState):
@@ -350,7 +356,6 @@ def get_q_values(
observations_resets: RNNObservation,
) -> chex.Array:
"""Forward pass to obtain q values."""
-
obs, resets = observations_resets
embedding = self.pre_torso(obs.agents_view)
@@ -373,7 +378,6 @@ def __call__(
"""Forward pass with additional construction of epsilon-greedy distribution.
When epsilon is not specified, we assume a greedy approach.
"""
-
obs, _ = observations_resets
hidden_state, q_values = self.get_q_values(hidden_state, observations_resets)
eps_greedy_dist = MaskedEpsGreedyDistribution(q_values, eps, obs.action_mask)
diff --git a/mava/systems/ppo/ff_ippo.py b/mava/systems/ppo/ff_ippo.py
index 7b45fb45f..8c426a251 100644
--- a/mava/systems/ppo/ff_ippo.py
+++ b/mava/systems/ppo/ff_ippo.py
@@ -54,7 +54,6 @@ def get_learner_fn(
config: DictConfig,
) -> LearnerFn[LearnerState]:
"""Get the learner function."""
-
# Get apply and update functions for actor and critic networks.
actor_apply_fn, critic_apply_fn = apply_fns
actor_update_fn, critic_update_fn = update_fns
@@ -68,6 +67,7 @@ def _update_step(learner_state: LearnerState, _: Any) -> Tuple[LearnerState, Tup
losses.
Args:
+ ----
learner_state (NamedTuple):
- params (Params): The current model parameters.
- opt_states (OptStates): The current optimizer states.
@@ -75,6 +75,7 @@ def _update_step(learner_state: LearnerState, _: Any) -> Tuple[LearnerState, Tup
- env_state (State): The environment state.
- last_timestep (TimeStep): The last timestep in the current trajectory.
_ (Any): The current metrics info.
+
"""
def _env_step(learner_state: LearnerState, _: Any) -> Tuple[LearnerState, PPOTransition]:
@@ -100,7 +101,13 @@ def _env_step(learner_state: LearnerState, _: Any) -> Tuple[LearnerState, PPOTra
info = timestep.extras["episode_metrics"]
transition = PPOTransition(
- done, action, value, timestep.reward, log_prob, last_timestep.observation, info
+ done,
+ action,
+ value,
+ timestep.reward,
+ log_prob,
+ last_timestep.observation,
+ info,
)
learner_state = LearnerState(params, opt_states, key, env_state, timestep)
return learner_state, transition
@@ -148,7 +155,6 @@ def _update_epoch(update_state: Tuple, _: Any) -> Tuple:
def _update_minibatch(train_state: Tuple, batch_info: Tuple) -> Tuple:
"""Update the network for a single minibatch."""
-
# UNPACK TRAIN STATE AND BATCH INFO
params, opt_states, key = train_state
traj_batch, advantages, targets = batch_info
@@ -220,7 +226,10 @@ def _critic_loss_fn(
# CALCULATE CRITIC LOSS
critic_grad_fn = jax.value_and_grad(_critic_loss_fn, has_aux=True)
critic_loss_info, critic_grads = critic_grad_fn(
- params.critic_params, opt_states.critic_opt_state, traj_batch, targets
+ params.critic_params,
+ opt_states.critic_opt_state,
+ traj_batch,
+ targets,
)
# Compute the parallel mean (pmean) over the batch.
@@ -284,7 +293,7 @@ def _critic_loss_fn(
lambda x: jnp.take(x, permutation, axis=0), batch
)
minibatches = jax.tree_util.tree_map(
- lambda x: jnp.reshape(x, [config.system.num_minibatches, -1] + list(x.shape[1:])),
+ lambda x: jnp.reshape(x, (config.system.num_minibatches, -1, *x.shape[1:])),
shuffled_batch,
)
@@ -316,14 +325,15 @@ def learner_fn(learner_state: LearnerState) -> ExperimentOutput[LearnerState]:
updates. The `_update_step` function is vectorized over a batch of inputs.
Args:
+ ----
learner_state (NamedTuple):
- params (Params): The initial model parameters.
- opt_states (OptStates): The initial optimizer state.
- key (chex.PRNGKey): The random number generator state.
- env_state (LogEnvState): The environment state.
- timesteps (TimeStep): The initial timestep in the initial trajectory.
- """
+ """
batched_update_step = jax.vmap(_update_step, in_axes=(0, None), axis_name="batch")
learner_state, (episode_info, loss_info) = jax.lax.scan(
@@ -427,7 +437,7 @@ def learner_setup(
replicate_learner = (params, opt_states, step_keys)
# Duplicate learner for update_batch_size.
- broadcast = lambda x: jnp.broadcast_to(x, (config.system.update_batch_size,) + x.shape)
+ broadcast = lambda x: jnp.broadcast_to(x, (config.system.update_batch_size, *x.shape))
replicate_learner = jax.tree_map(broadcast, replicate_learner)
# Duplicate learner across devices.
diff --git a/mava/systems/ppo/ff_mappo.py b/mava/systems/ppo/ff_mappo.py
index 519fa4f39..4c8f603ca 100644
--- a/mava/systems/ppo/ff_mappo.py
+++ b/mava/systems/ppo/ff_mappo.py
@@ -53,7 +53,6 @@ def get_learner_fn(
config: DictConfig,
) -> LearnerFn[LearnerState]:
"""Get the learner function."""
-
# Unpack apply and update functions.
actor_apply_fn, critic_apply_fn = apply_fns
actor_update_fn, critic_update_fn = update_fns
@@ -67,6 +66,7 @@ def _update_step(learner_state: LearnerState, _: Any) -> Tuple[LearnerState, Tup
losses.
Args:
+ ----
learner_state (NamedTuple):
- params (Params): The current model parameters.
- opt_states (OptStates): The current optimizer states.
@@ -74,6 +74,7 @@ def _update_step(learner_state: LearnerState, _: Any) -> Tuple[LearnerState, Tup
- env_state (State): The environment state.
- last_timestep (TimeStep): The last timestep in the current trajectory.
_ (Any): The current metrics info.
+
"""
def _env_step(learner_state: LearnerState, _: Any) -> Tuple[LearnerState, PPOTransition]:
@@ -98,7 +99,13 @@ def _env_step(learner_state: LearnerState, _: Any) -> Tuple[LearnerState, PPOTra
info = timestep.extras["episode_metrics"]
transition = PPOTransition(
- done, action, value, timestep.reward, log_prob, last_timestep.observation, info
+ done,
+ action,
+ value,
+ timestep.reward,
+ log_prob,
+ last_timestep.observation,
+ info,
)
learner_state = LearnerState(params, opt_states, key, env_state, timestep)
return learner_state, transition
@@ -146,7 +153,6 @@ def _update_epoch(update_state: Tuple, _: Any) -> Tuple:
def _update_minibatch(train_state: Tuple, batch_info: Tuple) -> Tuple:
"""Update the network for a single minibatch."""
-
# UNPACK TRAIN STATE AND BATCH INFO
params, opt_states, key = train_state
traj_batch, advantages, targets = batch_info
@@ -218,7 +224,10 @@ def _critic_loss_fn(
# CALCULATE CRITIC LOSS
critic_grad_fn = jax.value_and_grad(_critic_loss_fn, has_aux=True)
critic_loss_info, critic_grads = critic_grad_fn(
- params.critic_params, opt_states.critic_opt_state, traj_batch, targets
+ params.critic_params,
+ opt_states.critic_opt_state,
+ traj_batch,
+ targets,
)
# Compute the parallel mean (pmean) over the batch.
@@ -281,7 +290,7 @@ def _critic_loss_fn(
lambda x: jnp.take(x, permutation, axis=0), batch
)
minibatches = jax.tree_util.tree_map(
- lambda x: jnp.reshape(x, [config.system.num_minibatches, -1] + list(x.shape[1:])),
+ lambda x: jnp.reshape(x, (config.system.num_minibatches, -1, x.shape[1:])),
shuffled_batch,
)
@@ -313,14 +322,15 @@ def learner_fn(learner_state: LearnerState) -> ExperimentOutput[LearnerState]:
updates. The `_update_step` function is vectorized over a batch of inputs.
Args:
+ ----
learner_state (NamedTuple):
- params (Params): The initial model parameters.
- opt_states (OptStates): The initial optimizer states.
- key (chex.PRNGKey): The random number generator state.
- env_state (LogEnvState): The environment state.
- timesteps (TimeStep): The initial timestep in the initial trajectory.
- """
+ """
batched_update_step = jax.vmap(_update_step, in_axes=(0, None), axis_name="batch")
learner_state, (episode_info, loss_info) = jax.lax.scan(
@@ -424,7 +434,7 @@ def learner_setup(
replicate_learner = (params, opt_states, step_keys)
# Duplicate learner for update_batch_size.
- broadcast = lambda x: jnp.broadcast_to(x, (config.system.update_batch_size,) + x.shape)
+ broadcast = lambda x: jnp.broadcast_to(x, (config.system.update_batch_size, *x.shape))
replicate_learner = jax.tree_map(broadcast, replicate_learner)
# Duplicate learner across devices.
diff --git a/mava/systems/ppo/rec_ippo.py b/mava/systems/ppo/rec_ippo.py
index e70a59f07..508ee4196 100644
--- a/mava/systems/ppo/rec_ippo.py
+++ b/mava/systems/ppo/rec_ippo.py
@@ -57,7 +57,6 @@ def get_learner_fn(
config: DictConfig,
) -> LearnerFn[RNNLearnerState]:
"""Get the learner function."""
-
actor_apply_fn, critic_apply_fn = apply_fns
actor_update_fn, critic_update_fn = update_fns
@@ -70,6 +69,7 @@ def _update_step(learner_state: RNNLearnerState, _: Any) -> Tuple[RNNLearnerStat
losses.
Args:
+ ----
learner_state (NamedTuple):
- params (Params): The current model parameters.
- opt_states (OptStates): The current optimizer states.
@@ -79,6 +79,7 @@ def _update_step(learner_state: RNNLearnerState, _: Any) -> Tuple[RNNLearnerStat
- dones (bool): Whether the last timestep was a terminal state.
- hstates (HiddenStates): The current hidden states of the RNN.
_ (Any): The current metrics info.
+
"""
def _env_step(
@@ -183,10 +184,15 @@ def _calculate_gae(
traj_batch: RNNPPOTransition, last_val: chex.Array, last_done: chex.Array
) -> Tuple[chex.Array, chex.Array]:
def _get_advantages(
- carry: Tuple[chex.Array, chex.Array, chex.Array], transition: RNNPPOTransition
+ carry: Tuple[chex.Array, chex.Array, chex.Array],
+ transition: RNNPPOTransition,
) -> Tuple[Tuple[chex.Array, chex.Array, chex.Array], chex.Array]:
gae, next_value, next_done = carry
- done, value, reward = transition.done, transition.value, transition.reward
+ done, value, reward = (
+ transition.done,
+ transition.value,
+ transition.reward,
+ )
gamma = config.system.gamma
delta = reward + gamma * next_value * (1 - next_done) - value
gae = delta + gamma * config.system.gae_lambda * (1 - next_done) * gae
@@ -208,7 +214,6 @@ def _update_epoch(update_state: Tuple, _: Any) -> Tuple:
def _update_minibatch(train_state: Tuple, batch_info: Tuple) -> Tuple:
"""Update the network for a single minibatch."""
-
# UNPACK TRAIN STATE AND BATCH INFO
params, opt_states, key = train_state
traj_batch, advantages, targets = batch_info
@@ -225,7 +230,9 @@ def _actor_loss_fn(
obs_and_done = (traj_batch.obs, traj_batch.done)
_, actor_policy = actor_apply_fn(
- actor_params, traj_batch.hstates.policy_hidden_state[0], obs_and_done
+ actor_params,
+ traj_batch.hstates.policy_hidden_state[0],
+ obs_and_done,
)
log_prob = actor_policy.log_prob(traj_batch.action)
@@ -258,7 +265,9 @@ def _critic_loss_fn(
# RERUN NETWORK
obs_and_done = (traj_batch.obs, traj_batch.done)
_, value = critic_apply_fn(
- critic_params, traj_batch.hstates.critic_hidden_state[0], obs_and_done
+ critic_params,
+ traj_batch.hstates.critic_hidden_state[0],
+ obs_and_done,
)
# CALCULATE VALUE LOSS
@@ -286,7 +295,10 @@ def _critic_loss_fn(
# CALCULATE CRITIC LOSS
critic_grad_fn = jax.value_and_grad(_critic_loss_fn, has_aux=True)
critic_loss_info, critic_grads = critic_grad_fn(
- params.critic_params, opt_states.critic_opt_state, traj_batch, targets
+ params.critic_params,
+ opt_states.critic_opt_state,
+ traj_batch,
+ targets,
)
# Compute the parallel mean (pmean) over the batch.
@@ -418,6 +430,7 @@ def learner_fn(learner_state: RNNLearnerState) -> ExperimentOutput[RNNLearnerSta
updates. The `_update_step` function is vectorized over a batch of inputs.
Args:
+ ----
learner_state (NamedTuple):
- params (Params): The initial model parameters.
- opt_states (OptStates): The initial optimizer states.
@@ -426,8 +439,8 @@ def learner_fn(learner_state: RNNLearnerState) -> ExperimentOutput[RNNLearnerSta
- timesteps (TimeStep): The initial timestep in the initial trajectory.
- dones (bool): Whether the initial timestep was a terminal state.
- hstateS (HiddenStates): The initial hidden states of the RNN.
- """
+ """
batched_update_step = jax.vmap(_update_step, in_axes=(0, None), axis_name="batch")
learner_state, (episode_info, loss_info) = jax.lax.scan(
@@ -563,7 +576,7 @@ def learner_setup(
replicate_learner = (params, opt_states, hstates, step_keys, dones)
# Duplicate learner for update_batch_size.
- broadcast = lambda x: jnp.broadcast_to(x, (config.system.update_batch_size,) + x.shape)
+ broadcast = lambda x: jnp.broadcast_to(x, (config.system.update_batch_size, *x.shape))
replicate_learner = jax.tree_map(broadcast, replicate_learner)
# Duplicate learner across devices.
@@ -583,7 +596,7 @@ def learner_setup(
return learn, actor_network, init_learner_state
-def run_experiment(_config: DictConfig) -> float: # noqa: CCR001
+def run_experiment(_config: DictConfig) -> float:
"""Runs experiment."""
config = copy.deepcopy(_config)
diff --git a/mava/systems/ppo/rec_mappo.py b/mava/systems/ppo/rec_mappo.py
index 14284cedb..b40d33781 100644
--- a/mava/systems/ppo/rec_mappo.py
+++ b/mava/systems/ppo/rec_mappo.py
@@ -57,7 +57,6 @@ def get_learner_fn(
config: DictConfig,
) -> LearnerFn[RNNLearnerState]:
"""Get the learner function."""
-
actor_apply_fn, critic_apply_fn = apply_fns
actor_update_fn, critic_update_fn = update_fns
@@ -70,6 +69,7 @@ def _update_step(learner_state: RNNLearnerState, _: Any) -> Tuple[RNNLearnerStat
losses.
Args:
+ ----
learner_state (NamedTuple):
- params (Params): The current model parameters.
- opt_states (OptStates): The current optimizer states.
@@ -79,6 +79,7 @@ def _update_step(learner_state: RNNLearnerState, _: Any) -> Tuple[RNNLearnerStat
- last_done (bool): Whether the last timestep was a terminal state.
- hstates (HiddenStates): The hidden state of the policy and critic RNN.
_ (Any): The current metrics info.
+
"""
def _env_step(
@@ -115,7 +116,11 @@ def _env_step(
action = actor_policy.sample(seed=policy_key)
log_prob = actor_policy.log_prob(action)
- action, log_prob, value = (action.squeeze(0), log_prob.squeeze(0), value.squeeze(0))
+ action, log_prob, value = (
+ action.squeeze(0),
+ log_prob.squeeze(0),
+ value.squeeze(0),
+ )
# Step the environment.
env_state, timestep = jax.vmap(env.step, in_axes=(0, 0))(env_state, action)
@@ -175,10 +180,15 @@ def _calculate_gae(
traj_batch: RNNPPOTransition, last_val: chex.Array, last_done: chex.Array
) -> Tuple[chex.Array, chex.Array]:
def _get_advantages(
- carry: Tuple[chex.Array, chex.Array, chex.Array], transition: RNNPPOTransition
+ carry: Tuple[chex.Array, chex.Array, chex.Array],
+ transition: RNNPPOTransition,
) -> Tuple[Tuple[chex.Array, chex.Array, chex.Array], chex.Array]:
gae, next_value, next_done = carry
- done, value, reward = transition.done, transition.value, transition.reward
+ done, value, reward = (
+ transition.done,
+ transition.value,
+ transition.reward,
+ )
gamma = config.system.gamma
delta = reward + gamma * next_value * (1 - next_done) - value
gae = delta + gamma * config.system.gae_lambda * (1 - next_done) * gae
@@ -200,7 +210,6 @@ def _update_epoch(update_state: Tuple, _: Any) -> Tuple:
def _update_minibatch(train_state: Tuple, batch_info: Tuple) -> Tuple:
"""Update the network for a single minibatch."""
-
# UNPACK TRAIN STATE AND BATCH INFO
params, opt_states, key = train_state
traj_batch, advantages, targets = batch_info
@@ -216,7 +225,9 @@ def _actor_loss_fn(
# RERUN NETWORK
obs_and_done = (traj_batch.obs, traj_batch.done)
_, actor_policy = actor_apply_fn(
- actor_params, traj_batch.hstates.policy_hidden_state[0], obs_and_done
+ actor_params,
+ traj_batch.hstates.policy_hidden_state[0],
+ obs_and_done,
)
log_prob = actor_policy.log_prob(traj_batch.action)
@@ -249,7 +260,9 @@ def _critic_loss_fn(
# RERUN NETWORK
obs_and_done = (traj_batch.obs, traj_batch.done)
_, value = critic_apply_fn(
- critic_params, traj_batch.hstates.critic_hidden_state[0], obs_and_done
+ critic_params,
+ traj_batch.hstates.critic_hidden_state[0],
+ obs_and_done,
)
# CALCULATE VALUE LOSS
@@ -277,7 +290,10 @@ def _critic_loss_fn(
# CALCULATE CRITIC LOSS
critic_grad_fn = jax.value_and_grad(_critic_loss_fn, has_aux=True)
critic_loss_info, critic_grads = critic_grad_fn(
- params.critic_params, opt_states.critic_opt_state, traj_batch, targets
+ params.critic_params,
+ opt_states.critic_opt_state,
+ traj_batch,
+ targets,
)
# Compute the parallel mean (pmean) over the batch.
@@ -409,6 +425,7 @@ def learner_fn(learner_state: RNNLearnerState) -> ExperimentOutput[RNNLearnerSta
updates. The `_update_step` function is vectorized over a batch of inputs.
Args:
+ ----
learner_state (NamedTuple):
- params (Params): The initial model parameters.
- opt_states (OptStates): The initial optimizer states.
@@ -417,8 +434,8 @@ def learner_fn(learner_state: RNNLearnerState) -> ExperimentOutput[RNNLearnerSta
- timesteps (TimeStep): The initial timestep in the initial trajectory.
- dones (bool): Whether the initial timestep was a terminal state.
- hstates (HiddenStates): The hidden state of the policy and critic RNN.
- """
+ """
batched_update_step = jax.vmap(_update_step, in_axes=(0, None), axis_name="batch")
learner_state, (episode_info, loss_info) = jax.lax.scan(
@@ -555,7 +572,7 @@ def learner_setup(
replicate_learner = (params, opt_states, hstates, step_keys, dones)
# Duplicate learner for update_batch_size.
- broadcast = lambda x: jnp.broadcast_to(x, (config.system.update_batch_size,) + x.shape)
+ broadcast = lambda x: jnp.broadcast_to(x, (config.system.update_batch_size, *x.shape))
replicate_learner = jax.tree_map(broadcast, replicate_learner)
# Duplicate learner across devices.
@@ -575,7 +592,7 @@ def learner_setup(
return learn, actor_network, init_learner_state
-def run_experiment(_config: DictConfig) -> float: # noqa: CCR001
+def run_experiment(_config: DictConfig) -> float:
"""Runs experiment."""
config = copy.deepcopy(_config)
@@ -726,7 +743,11 @@ def run_experiment(_config: DictConfig) -> float: # noqa: CCR001
return eval_performance
-@hydra.main(config_path="../../configs", config_name="default_rec_mappo.yaml", version_base="1.2")
+@hydra.main(
+ config_path="../../configs",
+ config_name="default_rec_mappo.yaml",
+ version_base="1.2",
+)
def hydra_entry_point(cfg: DictConfig) -> float:
"""Experiment entry point."""
# Allow dynamic attributes.
diff --git a/mava/systems/q_learning/rec_iql.py b/mava/systems/q_learning/rec_iql.py
index 6be8e61a4..df601bdc1 100644
--- a/mava/systems/q_learning/rec_iql.py
+++ b/mava/systems/q_learning/rec_iql.py
@@ -70,9 +70,11 @@ def init(
"""Initialize system by creating the envs, networks etc.
Args:
+ ----
cfg: System configuration.
Returns:
+ -------
Tuple containing:
Tuple[Environment, Environment]: The environment and evaluation environment.
RecQNetwork: Recurrent Q network.
@@ -81,6 +83,7 @@ def init(
LearnerState: The initial learner state.
MavaLogger: The logger.
PRNGKey: The random key.
+
"""
logger = MavaLogger(cfg)
@@ -166,7 +169,12 @@ def replicate(x: Any) -> Any:
# Keys to reset env
n_keys = cfg.arch.num_envs * cfg.arch.n_devices * cfg.system.update_batch_size
- key_shape = (cfg.arch.n_devices, cfg.system.update_batch_size, cfg.arch.num_envs, -1)
+ key_shape = (
+ cfg.arch.n_devices,
+ cfg.system.update_batch_size,
+ cfg.arch.num_envs,
+ -1,
+ )
key, reset_key = jax.random.split(key)
reset_keys = jax.random.split(reset_key, n_keys)
reset_keys = jnp.reshape(reset_keys, key_shape)
@@ -219,6 +227,7 @@ def make_update_fns(
"""Create the update function for the Q-learner.
Args:
+ ----
cfg: System configuration.
env: Learning environment.
q_net: Recurrent q network.
@@ -226,29 +235,36 @@ def make_update_fns(
rb: The replay buffer.
Returns:
+ -------
The update function.
- """
+ """
# ---- Acting functions ----
def select_eps_greedy_action(
- action_selection_state: ActionSelectionState, obs: Observation, term_or_trunc: Array
+ action_selection_state: ActionSelectionState,
+ obs: Observation,
+ term_or_trunc: Array,
) -> Tuple[ActionSelectionState, Array]:
"""Select action to take in epsilon-greedy way. Batch and agent dims are included.
- Args:
+ Args:
+ ----
action_selection_state: Tuple of online parameters, previous hidden state,
environment timestep (used to calculate epsilon) and a random key.
obs: The observation from the previous timestep.
term_or_trunc: The flag timestep.last() from the previous timestep.
Returns:
+ -------
A tuple of the updated action selection state and the chosen action.
+
"""
params, hidden_state, t, key = action_selection_state
eps = jax.numpy.maximum(
- cfg.system.eps_min, 1 - (t / cfg.system.eps_decay) * (1 - cfg.system.eps_min)
+ cfg.system.eps_min,
+ 1 - (t / cfg.system.eps_decay) * (1 - cfg.system.eps_min),
)
obs = jax.tree_util.tree_map(lambda x: x[jnp.newaxis, ...], obs)
@@ -272,7 +288,14 @@ def select_eps_greedy_action(
def action_step(action_state: ActionState, _: Any) -> Tuple[ActionState, Dict]:
"""Selects action, steps env, stores timesteps in rb and repacks the parameters."""
# Unpack
- action_selection_state, env_state, buffer_state, obs, terminal, term_or_trunc = action_state
+ (
+ action_selection_state,
+ env_state,
+ buffer_state,
+ obs,
+ terminal,
+ term_or_trunc,
+ ) = action_state
# select the actions to take
next_action_selection_state, action = select_eps_greedy_action(
@@ -286,7 +309,12 @@ def action_step(action_state: ActionState, _: Any) -> Tuple[ActionState, Dict]:
reward = next_timestep.reward
transition = Transition(
- obs, action, reward, terminal, term_or_trunc, next_timestep.extras["real_next_obs"]
+ obs,
+ action,
+ reward,
+ terminal,
+ term_or_trunc,
+ next_timestep.extras["real_next_obs"],
)
# Add dummy time dim
transition = jax.tree_util.tree_map(lambda x: x[:, jnp.newaxis, ...], transition)
@@ -319,9 +347,9 @@ def prep_inputs_to_scannedrnn(obs: Observation, term_or_trunc: chex.Array) -> ch
Mostly swaps leading axes because the replay buffer outputs (B, T, ... )
and the RNN takes in (T, B, ...).
"""
-
hidden_state = ScannedRNN.initialize_carry(
- (cfg.system.sample_batch_size, obs.agents_view.shape[2]), cfg.network.hidden_state_dim
+ (cfg.system.sample_batch_size, obs.agents_view.shape[2]),
+ cfg.network.hidden_state_dim,
)
# the rb outputs (B, T, ... ) the RNN takes in (T, B, ...)
obs = switch_leading_axes(obs) # (B, T) -> (T, B)
@@ -337,7 +365,6 @@ def q_loss_fn(
action: Array,
target: Array,
) -> Tuple[Array, Metrics]:
-
# axes switched here to scan over time
hidden_state, obs_term_or_trunc = prep_inputs_to_scannedrnn(obs, term_or_trunc)
@@ -366,7 +393,6 @@ def update_q(
params: QNetParams, opt_states: optax.OptState, data: Transition, t_train: int
) -> Tuple[QNetParams, optax.OptState, Metrics]:
"""Update the Q parameters."""
-
# Get data aligned with current/next timestep
data_first = jax.tree_map(lambda x: x[:, :-1, ...], data)
data_next = jax.tree_map(lambda x: x[:, 1:, ...], data)
@@ -404,7 +430,8 @@ def update_q(
# Double q-value selection
next_q_val = jnp.squeeze(
- jnp.take_along_axis(next_q_vals_target, next_action[..., jnp.newaxis], axis=-1), axis=-1
+ jnp.take_along_axis(next_q_vals_target, next_action[..., jnp.newaxis], axis=-1),
+ axis=-1,
)
next_q_val = switch_leading_axes(next_q_val) # (T, B, ...) -> (B, T, ...)
@@ -438,7 +465,6 @@ def update_q(
def train(train_state: TrainState, _: Any) -> Tuple[TrainState, Metrics]:
"""Sample, train and repack."""
-
# unpack and get keys
buffer_state, params, opt_states, t_train, key = train_state
next_key, buff_key = jax.random.split(key, 2)
@@ -465,7 +491,6 @@ def update_step(
learner_state: LearnerState, _: Any
) -> Tuple[LearnerState, Tuple[Metrics, Metrics]]:
"""Interact, then learn."""
-
# unpack and get random keys
(
obs,
@@ -487,7 +512,12 @@ def update_step(
params.online, hidden_state, time_steps, act_key
)
action_state = ActionState(
- action_selection_state, env_state, buffer_state, obs, terminal, term_or_trunc
+ action_selection_state,
+ env_state,
+ buffer_state,
+ obs,
+ terminal,
+ term_or_trunc,
)
final_action_state, metrics = scanned_act(action_state)
@@ -580,7 +610,8 @@ def run_experiment(cfg: DictConfig) -> float:
# learn steps is not included in the loop counter.
elapsed_time = time.time() - start_time
eps = jax.numpy.maximum(
- cfg.system.eps_min, 1 - (t / cfg.system.eps_decay) * (1 - cfg.system.eps_min)
+ cfg.system.eps_min,
+ 1 - (t / cfg.system.eps_decay) * (1 - cfg.system.eps_min),
)
final_metrics, ep_completed = episode_metrics.get_final_step_metrics(metrics)
final_metrics["steps_per_second"] = steps_per_rollout / elapsed_time
diff --git a/mava/systems/sac/ff_isac.py b/mava/systems/sac/ff_isac.py
index 2c33028d1..6e7f99dc9 100644
--- a/mava/systems/sac/ff_isac.py
+++ b/mava/systems/sac/ff_isac.py
@@ -70,9 +70,11 @@ def init(
"""Initialize system by creating the envs, networks etc.
Args:
+ ----
cfg: System configuration.
Returns:
+ -------
Tuple containing:
Tuple[Environment, Environment]: The environment and evaluation environment.
Networks: Tuple of actor and critic networks.
@@ -82,6 +84,7 @@ def init(
Array: The target entropy.
MavaLogger: The logger.
PRNGKey: The random key.
+
"""
logger = MavaLogger(cfg)
@@ -176,7 +179,12 @@ def replicate(x: Any) -> Any:
# Reset env.
n_keys = cfg.arch.num_envs * cfg.arch.n_devices * cfg.system.update_batch_size
- key_shape = (cfg.arch.n_devices, cfg.system.update_batch_size, cfg.arch.num_envs, -1)
+ key_shape = (
+ cfg.arch.n_devices,
+ cfg.system.update_batch_size,
+ cfg.arch.num_envs,
+ -1,
+ )
key, reset_key = jax.random.split(key)
reset_keys = jax.random.split(reset_key, n_keys)
reset_keys = jnp.reshape(reset_keys, key_shape)
@@ -200,7 +208,16 @@ def replicate(x: Any) -> Any:
learner_state = LearnerState(
first_obs, env_state, buffer_state, params, opt_states, t, first_keys
)
- return (env, eval_env), networks, optims, rb, learner_state, target_entropy, logger, key
+ return (
+ (env, eval_env),
+ networks,
+ optims,
+ rb,
+ learner_state,
+ target_entropy,
+ logger,
+ key,
+ )
def make_update_fns(
@@ -217,6 +234,7 @@ def make_update_fns(
"""Create the update functions for the learner.
Args:
+ ----
cfg: System configuration.
env: The environment.
networks: Tuple of actor and critic networks.
@@ -225,9 +243,11 @@ def make_update_fns(
target_entropy: The target entropy.
Returns:
+ -------
Tuple of (explore_fn, update_fn).
Explore function is used for initial exploration with random actions.
Update function is the main learning function, it both acts and learns.
+
"""
actor_net, q_net = networks
actor_opt, q_opt, alpha_opt = optims
@@ -343,7 +363,11 @@ def update_actor_and_alpha(
# Update actor.
actor_grad_fn = jax.value_and_grad(actor_loss_fn)
actor_loss, act_grads = actor_grad_fn(
- params.actor, data.obs, jnp.exp(params.log_alpha), params.q.online, actor_key
+ params.actor,
+ data.obs,
+ jnp.exp(params.log_alpha),
+ params.q.online,
+ actor_key,
)
# Mean over the device and batch dimensions.
actor_loss, act_grads = lax.pmean((actor_loss, act_grads), axis_name="device")
@@ -408,7 +432,8 @@ def train(
return (buffer_state, params, opt_states, t, key), losses
def act(
- carry: Tuple[FrozenVariableDict, Array, State, BufferState, chex.PRNGKey], _: Any
+ carry: Tuple[FrozenVariableDict, Array, State, BufferState, chex.PRNGKey],
+ _: Any,
) -> Tuple[Tuple[FrozenVariableDict, Array, State, BufferState, chex.PRNGKey], Dict]:
"""Acting loop: select action, step env, add to buffer."""
actor_params, obs, env_state, buffer_state, key = carry
@@ -440,7 +465,6 @@ def explore(carry: LearnerState, _: Any) -> Tuple[LearnerState, Metrics]:
# Act loop -> sample -> update loop
def update_step(carry: LearnerState, _: Any) -> Tuple[LearnerState, Tuple[Metrics, Metrics]]:
"""Act, sample, learn. The body of the main SAC loop."""
-
obs, env_state, buffer_state, params, opt_states, t, key = carry
key, act_key, learn_key = jax.random.split(key, 3)
# Act
@@ -497,7 +521,16 @@ def run_experiment(cfg: DictConfig) -> float:
pprint(OmegaConf.to_container(cfg, resolve=True))
# Initialize system and make learning functions.
- (env, eval_env), networks, optims, rb, learner_state, target_entropy, logger, key = init(cfg)
+ (
+ (env, eval_env),
+ networks,
+ optims,
+ rb,
+ learner_state,
+ target_entropy,
+ logger,
+ key,
+ ) = init(cfg)
explore, update = make_update_fns(cfg, env, networks, optims, rb, target_entropy)
actor, _ = networks
diff --git a/mava/systems/sac/ff_masac.py b/mava/systems/sac/ff_masac.py
index 4401906ee..6b18fde66 100644
--- a/mava/systems/sac/ff_masac.py
+++ b/mava/systems/sac/ff_masac.py
@@ -71,9 +71,11 @@ def init(
"""Initialize system by creating the envs, networks etc.
Args:
+ ----
cfg: System configuration.
Returns:
+ -------
Tuple containing:
Tuple[Environment, Environment]: The environment and evaluation environment.
Networks: Tuple of actor and critic networks.
@@ -83,6 +85,7 @@ def init(
Array: The target entropy.
MavaLogger: The logger.
PRNGKey: The random key.
+
"""
logger = MavaLogger(cfg)
@@ -179,7 +182,12 @@ def replicate(x: Any) -> Any:
# Reset env.
n_keys = cfg.arch.num_envs * cfg.arch.n_devices * cfg.system.update_batch_size
- key_shape = (cfg.arch.n_devices, cfg.system.update_batch_size, cfg.arch.num_envs, -1)
+ key_shape = (
+ cfg.arch.n_devices,
+ cfg.system.update_batch_size,
+ cfg.arch.num_envs,
+ -1,
+ )
key, reset_key = jax.random.split(key)
reset_keys = jax.random.split(reset_key, n_keys)
reset_keys = jnp.reshape(reset_keys, key_shape)
@@ -203,7 +211,16 @@ def replicate(x: Any) -> Any:
learner_state = LearnerState(
first_obs, env_state, buffer_state, params, opt_states, t, first_keys
)
- return (env, eval_env), networks, optims, rb, learner_state, target_entropy, logger, key
+ return (
+ (env, eval_env),
+ networks,
+ optims,
+ rb,
+ learner_state,
+ target_entropy,
+ logger,
+ key,
+ )
def make_update_fns(
@@ -220,6 +237,7 @@ def make_update_fns(
"""Create the update functions for the learner.
Args:
+ ----
cfg: System configuration.
env: The environment.
networks: Tuple of actor and critic networks.
@@ -228,9 +246,11 @@ def make_update_fns(
target_entropy: The target entropy.
Returns:
+ -------
Tuple of (explore_fn, update_fn).
Explore function is used for initial exploration with random actions.
Update function is the main learning function, it both acts and learns.
+
"""
actor_net, q_net = networks
actor_opt, q_opt, alpha_opt = optims
@@ -238,7 +258,10 @@ def make_update_fns(
full_action_shape = (cfg.arch.num_envs, *env.action_spec().shape)
def step(
- action: Array, obs: ObservationGlobalState, env_state: State, buffer_state: BufferState
+ action: Array,
+ obs: ObservationGlobalState,
+ env_state: State,
+ buffer_state: BufferState,
) -> Tuple[Array, State, BufferState, Dict]:
"""Given an action, step the environment and add to the buffer."""
env_state, timestep = jax.vmap(env.step)(env_state, action)
@@ -425,7 +448,8 @@ def train(
return (buffer_state, params, opt_states, t, key), losses
def act(
- carry: Tuple[FrozenVariableDict, Array, State, BufferState, chex.PRNGKey], _: Any
+ carry: Tuple[FrozenVariableDict, Array, State, BufferState, chex.PRNGKey],
+ _: Any,
) -> Tuple[Tuple[FrozenVariableDict, Array, State, BufferState, chex.PRNGKey], Dict]:
"""Acting loop: select action, step env, add to buffer."""
actor_params, obs, env_state, buffer_state, key = carry
@@ -458,7 +482,6 @@ def explore(carry: LearnerState, _: Any) -> Tuple[LearnerState, Metrics]:
# Act loop -> sample -> update loop
def update_step(carry: LearnerState, _: Any) -> Tuple[LearnerState, Tuple[Metrics, Metrics]]:
"""Act, sample, learn. The body of the main SAC loop."""
-
obs, env_state, buffer_state, params, opt_states, t, key = carry
key, act_key, learn_key = jax.random.split(key, 3)
# Act
@@ -515,7 +538,16 @@ def run_experiment(cfg: DictConfig) -> float:
pprint(OmegaConf.to_container(cfg, resolve=True))
# Initialize system and make learning functions.
- (env, eval_env), networks, optims, rb, learner_state, target_entropy, logger, key = init(cfg)
+ (
+ (env, eval_env),
+ networks,
+ optims,
+ rb,
+ learner_state,
+ target_entropy,
+ logger,
+ key,
+ ) = init(cfg)
explore, update = make_update_fns(cfg, env, networks, optims, rb, target_entropy)
actor, _ = networks
diff --git a/mava/systems/sac/types.py b/mava/systems/sac/types.py
index 55f516ec0..b3d79e12f 100644
--- a/mava/systems/sac/types.py
+++ b/mava/systems/sac/types.py
@@ -26,7 +26,9 @@
Metrics: TypeAlias = Dict[str, Array]
Networks: TypeAlias = Tuple[nn.Module, nn.Module]
Optimisers: TypeAlias = Tuple[
- optax.GradientTransformation, optax.GradientTransformation, optax.GradientTransformation
+ optax.GradientTransformation,
+ optax.GradientTransformation,
+ optax.GradientTransformation,
]
diff --git a/mava/utils/centralised_training.py b/mava/utils/centralised_training.py
index 97d4ae37a..0abb7ae6b 100644
--- a/mava/utils/centralised_training.py
+++ b/mava/utils/centralised_training.py
@@ -22,17 +22,18 @@
def get_joint_action(actions: Array) -> Array:
- """
- Get the joint action from the individual actions of the agents.
+ """Get the joint action from the individual actions of the agents.
Joint actions are simply the concatenation of all agents actions.
Shapes are transformed from (B, A, Act) -> (B, A, A * Act).
Note: this returns the same joint action tiled for each agent.
Args:
+ ----
actions (B, A, Act): the individual actions of the agents.
Returns: (B, A, A * Act): the joint action repeated for each agent.
+
"""
batch_size, num_agents, act_size = actions.shape
repeated_action = jnp.tile(actions[:, jnp.newaxis, ...], (1, num_agents, 1, 1))
@@ -40,8 +41,7 @@ def get_joint_action(actions: Array) -> Array:
def get_updated_joint_actions(rb_actions: Array, policy_actions: Array) -> Array:
- """
- Get the updated joint actions by replacing the actions from the replay buffer with the new
+ """Get the updated joint actions by replacing the actions from the replay buffer with the new
actions from the policy. Only update joint action i with the new action for agent i.
The effect of this is that each agent's central critic sees what all other agents did in the
@@ -54,6 +54,7 @@ def get_updated_joint_actions(rb_actions: Array, policy_actions: Array) -> Array
Finally join the last two dimensions to get (B, A, A * Act).
Example:
+ -------
Given an action dim of 1, batch size of 1 and 3 agents.
All agents action may look like this: [0, 1, 2].
It is then repeated num agent times:
@@ -73,12 +74,13 @@ def get_updated_joint_actions(rb_actions: Array, policy_actions: Array) -> Array
but given an action dim > 1 you would need to join the last two dims.
Args:
+ ----
rb_actions (B, A, Act): the actions from the replay buffer.
policy_actions (B, A, Act): the new actions from the policy.
Returns: (B, A, A * Act): the updated joint actions.
- """
+ """
batch_size, num_agents, act_size = rb_actions.shape
# Repeat the actions from the replay buffer such that you have (B, A, A, Act).
diff --git a/mava/utils/checkpointing.py b/mava/utils/checkpointing.py
index 8955f76ce..10a552bde 100644
--- a/mava/utils/checkpointing.py
+++ b/mava/utils/checkpointing.py
@@ -49,6 +49,7 @@ def __init__(
"""Initialise the checkpointer tool
Args:
+ ----
model_name (str): Name of the model to be saved.
metadata (Optional[Dict], optional):
For storing model metadata. Defaults to None.
@@ -64,8 +65,8 @@ def __init__(
keep_period (Optional[int], optional):
If set, will not delete any checkpoint where
checkpoint_step % keep_period == 0. Defaults to None.
- """
+ """
# When we load an existing checkpoint, the sharding info is read from the checkpoint file,
# rather than from 'RestoreArgs'. This is desired behaviour, so we suppress the warning.
warnings.filterwarnings(
@@ -121,6 +122,7 @@ def save(
"""Save the learner state.
Args:
+ ----
timestep (int):
timestep at which the state is being saved.
unreplicated_learner_state (MavaState)
@@ -130,7 +132,9 @@ def save(
Defaults to 0.0.
Returns:
+ -------
bool: whether the saving was successful.
+
"""
model_save_success: bool = self._manager.save(
step=timestep,
@@ -152,6 +156,7 @@ def restore_params(
"""Restore the params and the hidden state (in case of RNNs)
Args:
+ ----
input_params (Any): A pytree of FrozenDict params of the learner.
timestep (Optional[int]):
Specific timestep for restoration (of course, only if that timestep exists).
@@ -160,7 +165,9 @@ def restore_params(
THiddenState (Type): The type of the hidden states to be restored.
Returns:
+ -------
Tuple[Params,Union[HiddenState, None]]: the restored params and hidden states.
+
"""
# We want to ensure `major` versions match, but allow `minor` versions to differ
# i.e. v0.1 and 0.2 are compatible, but v1.0 and v2.0 are not
@@ -202,7 +209,9 @@ def restore_params(
def get_cfg(self) -> DictConfig:
"""Return the metadata of the checkpoint.
- Returns:
+ Returns
+ -------
DictConfig: metadata of the checkpoint.
+
"""
return DictConfig(self._manager.metadata())
diff --git a/mava/utils/jax_utils.py b/mava/utils/jax_utils.py
index 5fd498c92..cd397f251 100644
--- a/mava/utils/jax_utils.py
+++ b/mava/utils/jax_utils.py
@@ -33,10 +33,12 @@ def merge_leading_dims(x: chex.Array, num_dims: chex.Numeric) -> chex.Array:
"""Merge leading dimensions.
Note:
+ ----
This implementation is a generic function for merging leading dimensions
extracted from Haiku.
For the original implementation, please refer to the following link:
(https://github.com/deepmind/dm-haiku/blob/main/haiku/_src/basic.py#L207)
+
"""
# Don't merge if there aren't dimensions to merge.
if not ndim_at_least(x, num_dims):
diff --git a/mava/utils/logger.py b/mava/utils/logger.py
index 4edad361e..aa3464199 100644
--- a/mava/utils/logger.py
+++ b/mava/utils/logger.py
@@ -18,7 +18,7 @@
import zipfile
from datetime import datetime
from enum import Enum
-from typing import Dict, List, Union
+from typing import ClassVar, Dict, List, Union
import jax
import neptune
@@ -55,10 +55,12 @@ def log(self, metrics: Dict, t: int, t_eval: int, event: LogEvent) -> None:
"""Log a dictionary metrics at a given timestep.
Args:
+ ----
metrics (Dict): dictionary of metrics to log.
t (int): the current timestep.
t_eval (int): the number of previous evaluations.
event (LogEvent): the event that the metrics are associated with.
+
"""
# Ideally we want to avoid special metrics like this as much as possible.
# Might be better to calculate this outside as we want to keep the number of these
@@ -209,7 +211,7 @@ class JsonLogger(BaseLogger):
"""Json logger for marl-eval."""
# These are the only metrics that marl-eval needs to plot.
- _METRICS_TO_LOG = ["episode_return/mean", "win_rate", "steps_per_second"]
+ _METRICS_TO_LOG: ClassVar[List[str]] = ["episode_return/mean", "win_rate", "steps_per_second"]
def __init__(self, cfg: DictConfig, unique_token: str) -> None:
json_exp_path = get_logger_path(cfg, "json")
@@ -251,7 +253,7 @@ def log_stat(self, key: str, value: float, step: int, eval_step: int, event: Log
class ConsoleLogger(BaseLogger):
"""Logger for writing to stdout."""
- _EVENT_COLOURS = {
+ _EVENT_COLOURS: ClassVar[Dict[LogEvent, str]] = {
LogEvent.TRAIN: Fore.MAGENTA,
LogEvent.EVAL: Fore.GREEN,
LogEvent.ABSOLUTE: Fore.BLUE,
@@ -299,7 +301,6 @@ def log_dict(self, data: Dict, step: int, eval_step: int, event: LogEvent) -> No
def _make_multi_logger(cfg: DictConfig) -> BaseLogger:
"""Creates a MultiLogger given a config"""
-
loggers: List[BaseLogger] = []
unique_token = datetime.now().strftime("%Y%m%d%H%M%S")
@@ -336,7 +337,6 @@ def get_logger_path(config: DictConfig, logger_type: str) -> str:
def describe(x: ArrayLike) -> Union[Dict[str, ArrayLike], ArrayLike]:
"""Generate summary statistics for an array of metrics (mean, std, min, max)."""
-
if not isinstance(x, jax.Array) or x.size <= 1:
return x
diff --git a/mava/utils/make_env.py b/mava/utils/make_env.py
index 39b348b40..b4e94ebd1 100644
--- a/mava/utils/make_env.py
+++ b/mava/utils/make_env.py
@@ -52,7 +52,10 @@
_jumanji_registry = {
"RobotWarehouse-v0": {"generator": RwareRandomGenerator, "wrapper": RwareWrapper},
"LevelBasedForaging-v0": {"generator": LbfRandomGenerator, "wrapper": LbfWrapper},
- "MaConnector-v2": {"generator": ConnectorRandomGenerator, "wrapper": ConnectorWrapper},
+ "MaConnector-v2": {
+ "generator": ConnectorRandomGenerator,
+ "wrapper": ConnectorWrapper,
+ },
"Cleaner-v0": {"generator": CleanerRandomGenerator, "wrapper": CleanerWrapper},
}
@@ -67,7 +70,6 @@
def add_extra_wrappers(
train_env: Environment, eval_env: Environment, config: DictConfig
) -> Environment:
-
# Disable the AgentID wrapper if the environment has implicit agent IDs.
config.system.add_agent_id = config.system.add_agent_id & (~config.env.implicit_agent_id)
@@ -84,16 +86,18 @@ def add_extra_wrappers(
def make_jumanji_env(
env_name: str, config: DictConfig, add_global_state: bool = False
) -> Tuple[Environment, Environment]:
- """
- Create a Jumanji environments for training and evaluation.
+ """Create a Jumanji environments for training and evaluation.
Args:
+ ----
env_name (str): The name of the environment to create.
config (Dict): The configuration of the environment.
add_global_state (bool): Whether to add the global state to the observation.
Returns:
+ -------
A tuple of the environments.
+
"""
# Config generator and select the wrapper.
generator = _jumanji_registry[env_name]["generator"]
@@ -114,18 +118,19 @@ def make_jumanji_env(
def make_jaxmarl_env(
env_name: str, config: DictConfig, add_global_state: bool = False
) -> Tuple[Environment, Environment]:
- """
- Create a JAXMARL environment.
+ """Create a JAXMARL environment.
Args:
+ ----
env_name (str): The name of the environment to create.
config (Dict): The configuration of the environment.
add_global_state (bool): Whether to add the global state to the observation.
Returns:
+ -------
A JAXMARL environment.
- """
+ """
kwargs = dict(config.env.kwargs)
if "smax" in env_name.lower():
kwargs["scenario"] = map_name_to_scenario(config.env.scenario.task_name)
@@ -148,16 +153,18 @@ def make_jaxmarl_env(
def make_matrax_env(
env_name: str, config: DictConfig, add_global_state: bool = False
) -> Tuple[Environment, Environment]:
- """
- Creates Matrax environments for training and evaluation.
+ """Creates Matrax environments for training and evaluation.
Args:
+ ----
env_name: The name of the environment to create.
config: The configuration of the environment.
add_global_state: Whether to add the global state to the observation.
Returns:
+ -------
A tuple containing a train and evaluation Matrax environment.
+
"""
# Select the Matrax wrapper.
wrapper = _matrax_registry[env_name]
@@ -176,16 +183,18 @@ def make_matrax_env(
def make_gigastep_env(
env_name: str, config: DictConfig, add_global_state: bool = False
) -> Tuple[Environment, Environment]:
- """
- Create a Gigastep environment.
+ """Create a Gigastep environment.
Args:
+ ----
env_name (str): The name of the environment to create.
config (Dict): The configuration of the environment.
add_global_state (bool): Whether to add the global state to the observation. Default False.
Returns:
+ -------
A tuple of the environments.
+
"""
wrapper = _gigastep_registry[env_name]
@@ -200,15 +209,17 @@ def make_gigastep_env(
def make(config: DictConfig, add_global_state: bool = False) -> Tuple[Environment, Environment]:
- """
- Create environments for training and evaluation..
+ """Create environments for training and evaluation..
Args:
+ ----
config (Dict): The configuration of the environment.
add_global_state (bool): Whether to add the global state to the observation.
Returns:
+ -------
A tuple of the environments.
+
"""
env_name = config.env.scenario.name
diff --git a/mava/utils/training.py b/mava/utils/training.py
index c509e6a61..77aa98fc6 100644
--- a/mava/utils/training.py
+++ b/mava/utils/training.py
@@ -21,14 +21,17 @@ def make_learning_rate_schedule(init_lr: float, config: DictConfig) -> Callable:
"""Makes a very simple linear learning rate scheduler.
Args:
+ ----
init_lr: initial learning rate.
config: system configuration.
Note:
+ ----
We use a simple linear learning rate scheduler based on the suggestions from a blog on PPO
implementation details which can be viewed at http://tinyurl.com/mr3chs4p
This function can be extended to have more complex learning rate schedules by adding any
relevant arguments to the system config and then parsing them accordingly here.
+
"""
def linear_scedule(count: int) -> float:
@@ -46,11 +49,14 @@ def make_learning_rate(init_lr: float, config: DictConfig) -> Union[float, Calla
"""Retuns a constant learning rate or a learning rate schedule.
Args:
+ ----
init_lr: initial learning rate.
config: system configuration.
Returns:
+ -------
A learning rate schedule or fixed learning rate.
+
"""
if config.system.decay_learning_rates:
return make_learning_rate_schedule(init_lr, config)
diff --git a/mava/wrappers/__init__.py b/mava/wrappers/__init__.py
index 91bf7b4c4..0c6f4753f 100644
--- a/mava/wrappers/__init__.py
+++ b/mava/wrappers/__init__.py
@@ -11,6 +11,7 @@
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
+# ruff: noqa: F401
from mava.wrappers.auto_reset_wrapper import AutoResetWrapper
from mava.wrappers.episode_metrics import RecordEpisodeMetrics
diff --git a/mava/wrappers/episode_metrics.py b/mava/wrappers/episode_metrics.py
index a2b0fdb37..93c1e49e7 100644
--- a/mava/wrappers/episode_metrics.py
+++ b/mava/wrappers/episode_metrics.py
@@ -93,7 +93,9 @@ def step(
return state, timestep
-def get_final_step_metrics(metrics: Dict[str, chex.Array]) -> Tuple[Dict[str, chex.Array], bool]:
+def get_final_step_metrics(
+ metrics: Dict[str, chex.Array],
+) -> Tuple[Dict[str, chex.Array], bool]:
"""Get the metrics for the final step of an episode and check if there was a final step
within the provided metrics.
diff --git a/mava/wrappers/gigastep.py b/mava/wrappers/gigastep.py
index fd982f924..e4f61bd60 100644
--- a/mava/wrappers/gigastep.py
+++ b/mava/wrappers/gigastep.py
@@ -48,13 +48,13 @@ def __init__(
env: GigastepEnv,
has_global_state: bool = False,
):
- """
- Args:
+ """Args:
+ ----
env: The Gigastep environment to be wrapped.
time_limit (int): The maximum duration of each episode, in seconds. Defaults to 500.
has_global_state (bool): Whether the environment has a global state. Defaults to False.
- """
+ """
super().__init__(env)
assert (
env.discrete_actions
@@ -70,18 +70,18 @@ def __init__(
self.has_global_state = has_global_state
def reset(self, key: PRNGKey) -> Tuple[GigastepState, TimeStep]:
- """
- Reset the Gigastep environment.
+ """Reset the Gigastep environment.
Args:
+ ----
key (PRNGKey): The PRNGKey.
Returns:
+ -------
GigastepState : the state of the environment.
TimeStep : the first time step.
"""
-
key, reset_key, adversary_key = jax.random.split(key, 3)
obs, state = self._env.reset(reset_key)
@@ -98,14 +98,15 @@ def reset(self, key: PRNGKey) -> Tuple[GigastepState, TimeStep]:
return state, timestep
def step(self, state: GigastepState, action: Array) -> Tuple[GigastepState, TimeStep]:
- """
- Takes a step in the Gigastep environment.
+ """Takes a step in the Gigastep environment.
Args:
+ ----
state (GigastepState): The current state of the environment.
action (Array): The actions for controllable agents.
Returns:
+ -------
Tuple[GigastepState, TimeStep]: A tuple containing the next state of the environment
and the next time step.
@@ -166,15 +167,17 @@ def action_mask(self) -> Array:
return jnp.ones((self.num_agents, self._env.n_actions)) # all actions are valid
def get_global_state(self, obs: Array) -> Array:
- """
- Combines observations from all agents and adversaries
+ """Combines observations from all agents and adversaries
to create a global state for the environment.
Args:
+ ----
obs (Array): The observations of all agents and adversaries.
Returns:
+ -------
global_obs (Array): The global observation.
+
"""
# the global observation needs to be tested once we have better heuristics for adversaries.
global_obs = jnp.concatenate(obs, axis=0)
@@ -196,7 +199,10 @@ def observation_spec(self) -> specs.Spec:
)
if self.has_global_state:
global_state = specs.BoundedArray(
- (self.num_agents, self._env.observation_space.shape[0] * self._env.n_agents),
+ (
+ self.num_agents,
+ self._env.observation_space.shape[0] * self._env.n_agents,
+ ),
jnp.int32,
0,
255,
@@ -227,22 +233,28 @@ def reward_spec(self) -> specs.Array:
def discount_spec(self) -> specs.BoundedArray:
return specs.BoundedArray(
- shape=(self.num_agents,), dtype=float, minimum=0.0, maximum=1.0, name="discount"
+ shape=(self.num_agents,),
+ dtype=float,
+ minimum=0.0,
+ maximum=1.0,
+ name="discount",
)
def _split_obs_and_state(
self, obs: Array, state: Tuple[Dict, Dict]
) -> Tuple[Array, Tuple[Dict, Dict], Array, Tuple[Dict, Dict]]:
- """
- Separates the observations and state for both teams.
+ """Separates the observations and state for both teams.
Args:
+ ----
obs (Array): The observations of all agents.
state (Tuple[Dict, Dict]): The state of all agents.
Returns:
+ -------
Tuple[Array, Tuple[Dict, Dict], Array, Tuple[Dict, Dict]]: Two tuples
representing observations and states for each team.
+
"""
# The first n_agents_team1 elements in each array belong to team1
team1_obs, team2_obs = obs[: self.num_agents], obs[self.num_agents :]
@@ -260,31 +272,35 @@ def _split_obs_and_state(
)
def won_episode(self, state: Tuple[Dict, Dict]) -> Array:
- """
- Determines the winning team.
+ """Determines the winning team.
The winning team is the one with more agents alive at the end.
Args:
+ ----
state (Tuple[Dict, Dict]): The state of all agents.
Returns:
+ -------
Array: Winning team indicator (1 if team_1 wins, 0 otherwise).
+
"""
# https://github.com/mlech26l/gigastep/blob/main/gigastep/evaluator.py#L261
alive = state[0]["alive"]
return jnp.sum(alive[: self.num_agents]) > jnp.sum(alive[self.num_agents :])
def adversary_policy(self, obs: Array, state: Tuple[Dict, Dict], key: PRNGKey) -> Array:
- """
- Generates actions for the adversary based on observations and state.
+ """Generates actions for the adversary based on observations and state.
Args:
+ ----
obs (Array): The observations of the adversary.
state (Tuple[Dict, Dict]): The state of the adversary.
key (PRNGKey): The pseudo-random number generator key.
Returns:
+ -------
Array: Actions for the adversary.
+
"""
return jax.random.randint(key, (obs.shape[0],), 0, self.num_actions)
diff --git a/mava/wrappers/jaxmarl.py b/mava/wrappers/jaxmarl.py
index 6f926bf66..738263254 100644
--- a/mava/wrappers/jaxmarl.py
+++ b/mava/wrappers/jaxmarl.py
@@ -79,14 +79,14 @@ def unbatchify(x: Array, agents: List[str]) -> Dict[str, Array]:
def merge_space(
- spec: Dict[str, Union[jaxmarl_spaces.Box, jaxmarl_spaces.Discrete]]
+ spec: Dict[str, Union[jaxmarl_spaces.Box, jaxmarl_spaces.Discrete]],
) -> jaxmarl_spaces.Space:
"""Convert a dictionary of spaces into a single space with a num_agents size first dimension.
JaxMarl uses a dictionary of specs, one per agent. For now we want this to be a single spec.
"""
n_agents = len(spec)
- single_spec = copy.deepcopy(list(spec.values())[0])
+ single_spec = copy.deepcopy(next(iter(spec.values())))
err = f"Unsupported space for merging spaces, expected Box or Discrete, got {type(single_spec)}"
assert _is_discrete(single_spec) or _is_box(single_spec), err
@@ -160,9 +160,7 @@ def jaxmarl_space_to_jumanji_spec(space: jaxmarl_spaces.Space) -> specs.Spec:
class JaxMarlWrapper(Wrapper, ABC):
- """
- A wrapper for JaxMarl environments to make their API compatible with Jumanji environments.
- """
+ """A wrapper for JaxMarl environments to make their API compatible with Jumanji environments."""
def __init__(
self,
@@ -170,13 +168,14 @@ def __init__(
has_global_state: bool,
timelimit: int,
) -> None:
- """
- Initialize the JaxMarlWrapper.
+ """Initialize the JaxMarlWrapper.
Args:
+ ----
- env: The JaxMarl environment to wrap.
- has_global_state: Whether the environment has global state.
- timelimit: The time limit for each episode.
+
"""
# Check that all specs are the same as we only support homogeneous environments, for now ;)
homogenous_error = (
@@ -193,9 +192,9 @@ def __init__(
self.has_global_state = has_global_state
# Calling these on init to cache the values in a non-jitted context.
- self.state_size
- self.action_dim
- self.num_agents
+ self.state_size # noqa: B018
+ self.action_dim # noqa: B018
+ self.num_agents # noqa: B018
def reset(
self, key: PRNGKey
@@ -287,7 +286,11 @@ def reward_spec(self) -> specs.Array:
def discount_spec(self) -> specs.BoundedArray:
return specs.BoundedArray(
- shape=(self.num_agents,), dtype=float, minimum=0.0, maximum=1.0, name="discount"
+ shape=(self.num_agents,),
+ dtype=float,
+ minimum=0.0,
+ maximum=1.0,
+ name="discount",
)
@abstractmethod
@@ -303,19 +306,19 @@ def get_global_state(self, wrapped_env_state: Any, obs: Dict[str, Array]) -> Arr
@cached_property
@abstractmethod
def num_agents(self) -> chex.Array:
- "Get the number of agents"
+ """Get the number of agents"""
...
@cached_property
@abstractmethod
def action_dim(self) -> chex.Array:
- "Get the actions dim for each agent."
+ """Get the actions dim for each agent."""
...
@cached_property
@abstractmethod
def state_size(self) -> chex.Array:
- "Get the sate size of the global observation"
+ """Get the sate size of the global observation"""
...
@@ -351,17 +354,17 @@ def step(
@cached_property
def state_size(self) -> chex.Array:
- "Get the sate size of the global observation"
+ """Get the sate size of the global observation"""
return self._env.state_size
@cached_property
def num_agents(self) -> chex.Array:
- "Get the number of agents"
+ """Get the number of agents"""
return self._env.num_agents
@cached_property
def action_dim(self) -> chex.Array:
- "Get the actions dim for each agent."
+ """Get the actions dim for each agent."""
single_agent_action_space = self._env.action_space(self.agents[0])
return single_agent_action_space.n
@@ -389,17 +392,17 @@ def __init__(
@cached_property
def num_agents(self) -> chex.Array:
- "Get the number of agents"
+ """Get the number of agents"""
return self._env.num_agents
@cached_property
def action_dim(self) -> chex.Array:
- "Get the actions dim for each agent."
+ """Get the actions dim for each agent."""
return self._env.action_space(self.agents[0]).shape[0]
@cached_property
def state_size(self) -> chex.Array:
- "Get the sate size of the global observation"
+ """Get the sate size of the global observation"""
brax_env = self._env.env
return brax_env.observation_size
diff --git a/mava/wrappers/jumanji.py b/mava/wrappers/jumanji.py
index 8a0f99176..0b9981081 100644
--- a/mava/wrappers/jumanji.py
+++ b/mava/wrappers/jumanji.py
@@ -90,7 +90,9 @@ def step(self, state: State, action: chex.Array) -> Tuple[State, TimeStep]:
return state, timestep
- def observation_spec(self) -> specs.Spec[Union[Observation, ObservationGlobalState]]:
+ def observation_spec(
+ self,
+ ) -> specs.Spec[Union[Observation, ObservationGlobalState]]:
"""Specification of the observation of the environment."""
step_count = specs.BoundedArray(
(self.num_agents,),
@@ -121,7 +123,7 @@ def observation_spec(self) -> specs.Spec[Union[Observation, ObservationGlobalSta
@cached_property
def action_dim(self) -> chex.Array:
- "Get the actions dim for each agent."
+ """Get the actions dim for each agent."""
return int(self._env.action_spec().num_values[0])
@@ -145,13 +147,14 @@ def modify_timestep(self, timestep: TimeStep) -> TimeStep[Observation]:
class LbfWrapper(MultiAgentWrapper):
- """
- Multi-agent wrapper for the Level-Based Foraging environment.
+ """Multi-agent wrapper for the Level-Based Foraging environment.
Args:
+ ----
env (Environment): The base environment.
use_individual_rewards (bool): If true each agent gets a separate reward,
sum reward otherwise.
+
"""
def __init__(
@@ -178,7 +181,6 @@ def modify_timestep(self, timestep: TimeStep) -> TimeStep[Observation]:
"""Modify the timestep for Level-Based Foraging environment and update
the reward based on the specified reward handling strategy.
"""
-
# Create a new observation with adjusted step count
modified_observation = Observation(
agents_view=timestep.observation.agents_view,
@@ -230,10 +232,11 @@ def get_global_state(self, obs: Observation) -> chex.Array:
"""Constructs the global state from the global information
in the agent observations (positions, targets and paths.)
"""
-
return obs.agents_view[..., :3]
- def observation_spec(self) -> specs.Spec[Union[Observation, ObservationGlobalState]]:
+ def observation_spec(
+ self,
+ ) -> specs.Spec[Union[Observation, ObservationGlobalState]]:
"""Specification of the observation of the environment."""
step_count = specs.BoundedArray(
(self.num_agents,),
@@ -257,7 +260,12 @@ def observation_spec(self) -> specs.Spec[Union[Observation, ObservationGlobalSta
if self.add_global_state:
global_state = specs.BoundedArray(
- shape=(self._env.num_agents, self._env.grid_size, self._env.grid_size, 3),
+ shape=(
+ self._env.num_agents,
+ self._env.grid_size,
+ self._env.grid_size,
+ 3,
+ ),
dtype=bool,
name="global_state",
minimum=False,
@@ -283,7 +291,6 @@ def create_agents_view(grid: chex.Array, agents_locations: chex.Array) -> chex.A
"""Create separate channels for dirty cells, wall cells and agent positions.
Also add a channel that marks an agent's own position.
"""
-
num_agents = self.num_agents
# A: Number of agents
@@ -330,7 +337,10 @@ def create_agents_view(grid: chex.Array, agents_locations: chex.Array) -> chex.A
extras = {"won_episode": timestep.extras["num_dirty_tiles"] == 0}
return timestep.replace(
- observation=Observation(**obs_data), reward=reward, discount=discount, extras=extras
+ observation=Observation(**obs_data),
+ reward=reward,
+ discount=discount,
+ extras=extras,
)
def get_global_state(self, obs: Observation) -> chex.Array:
@@ -339,7 +349,9 @@ def get_global_state(self, obs: Observation) -> chex.Array:
"""
return obs.agents_view[..., :3] # (A, R, C, 3)
- def observation_spec(self) -> specs.Spec[Union[Observation, ObservationGlobalState]]:
+ def observation_spec(
+ self,
+ ) -> specs.Spec[Union[Observation, ObservationGlobalState]]:
"""Specification of the observation of the environment."""
step_count = specs.BoundedArray(
(self.num_agents,),
diff --git a/mava/wrappers/matrax.py b/mava/wrappers/matrax.py
index 3b5a071aa..8b12a2307 100644
--- a/mava/wrappers/matrax.py
+++ b/mava/wrappers/matrax.py
@@ -61,7 +61,9 @@ def step(self, state: State, action: chex.Array) -> Tuple[State, TimeStep]:
state, timestep = self._env.step(state, action)
return state, self.modify_timestep(timestep)
- def observation_spec(self) -> specs.Spec[Union[Observation][ObservationGlobalState]]:
+ def observation_spec(
+ self,
+ ) -> specs.Spec[Union[Observation][ObservationGlobalState]]:
"""Specification of the observation of the environment."""
step_count = specs.BoundedArray(
(self._num_agents,),
diff --git a/pyproject.toml b/pyproject.toml
index f036225e2..f4038941b 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -1,9 +1,3 @@
-[tool.black]
-line-length = 100
-
-[tool.isort]
-profile = "black"
-
[tool.mypy]
python_version = 3.9
namespace_packages = true
@@ -34,35 +28,15 @@ module = [
"omegaconf.*",
]
-[tool.flake8]
-select = ["A","B","C","D","E","F","G","I","N","T","W"] # Specify list of error codes to report.
-exclude = [
- ".tox",
- ".git",
- "__pycache__",
- "build",
- "dist",
- "proto/*",
- "*.pyc",
- "*.egg-info",
- ".cache",
- ".eggs",
-]
-max-line-length=100
-max-cognitive-complexity=11
-import-order-style = "google"
-application-import-names = "mava"
-doctests = true
-docstring-convention = "google"
-per-file-ignores = "__init__.py:F401"
+[tool.ruff]
+line-length = 100
+[tool.ruff.lint]
+select = ["A", "B", "E", "F", "I", "N", "W", "RUF", "ANN"]
ignore = [
- "A002", # Argument shadowing a Python builtin.
- "A003", # Class attribute shadowing a Python builtin.
- "D107", # Do not require docstrings for __init__.
- "E266", # Do not require block comments to only have a single leading #.
- "E731", # Do not assign a lambda expression, use a def.
- "W503", # Line break before binary operator (not compatible with black).
- "B017", # assertRaises(Exception): or pytest.raises(Exception) should be considered evil.
- "E203", # black and flake8 disagree on whitespace before ':'.
+ "E731", # Allow lambdas to be assigned to variables.
+ "ANN101", # no need to type self
+ "ANN102", # no need to type cls
+ "ANN204", # no need for return type for special methods
+ "ANN401", # can use Any type
]
diff --git a/requirements/requirements-dev.txt b/requirements/requirements-dev.txt
index 8dddf6794..1b77fe665 100644
--- a/requirements/requirements-dev.txt
+++ b/requirements/requirements-dev.txt
@@ -1,9 +1,3 @@
-black==22.3.0
-coverage
-flake8==6.1.0
-importlib-metadata<5.0
-isort==5.11.5
-livereload
mkdocs==1.2.3
mkdocs-git-revision-date-plugin
mkdocs-include-markdown-plugin
@@ -11,15 +5,6 @@ mkdocs-material
mkdocs-mermaid2-plugin==0.6.0
mkdocstrings==0.18.0
mknotebooks==0.7.1
-mypy==0.991
-nbmake
-pre-commit==2.17.0
-promise
-pymdown-extensions
-pytest==7.0.1
-pytest-cov
-pytest-mock
-pytest-parallel
-pytest-xdist
-pytype
-testfixtures
+mypy
+pre-commit
+pytest