diff --git a/examples/clean_rl_gru_ppo/clean_rl_ppo_gru_discrete_actions_example.py b/examples/clean_rl_gru_ppo/clean_rl_ppo_gru_discrete_actions_example.py
new file mode 100644
index 00000000..89e496d9
--- /dev/null
+++ b/examples/clean_rl_gru_ppo/clean_rl_ppo_gru_discrete_actions_example.py
@@ -0,0 +1,444 @@
+# docs and experiment results can be found at https://docs.cleanrl.dev/rl-algorithms/ppo/#ppo_atari_lstmpy based on
+# https://github.com/vwxyzjn/cleanrl/blob/master/cleanrl/ppo_atari_lstm.py Modified to work with GDRL,
+# vector observation space, use GRU/Vanilla RNN with checkpoint saving/basic inference added, and other changes.
+# Hyperparameter defaults have been changed, feel free to adjust as needed.
+
+import os
+import pathlib
+import random
+import sys
+import time
+from collections import deque
+from dataclasses import dataclass
+from typing import Optional
+
+import gymnasium as gym
+import numpy as np
+import torch
+import torch.nn as nn
+import torch.optim as optim
+import tyro
+from torch.distributions.categorical import Categorical
+from torch.utils.tensorboard import SummaryWriter
+
+from godot_rl.wrappers.clean_rl_wrapper import CleanRLGodotEnv
+
+
+@dataclass
+class Args:
+    env_path: str = None
+    """Path to the Godot exported environment"""
+    n_parallel: int = 1
+    """How many instances of the environment executable to
+    launch (requires --env_path to be set if > 1)."""
+    viz: bool = False
+    """Whether the exported Godot environment will displayed during training"""
+    speedup: int = 8
+    """How much to speed up the environment"""
+    use_vanilla_rnn: bool = False
+    """If False, will use nn.GRU. If True, will use nn.RNN instead."""
+    exp_name: str = os.path.basename(__file__)[: -len(".py")]
+    """the name of this experiment"""
+    seed: int = 1
+    """seed of the experiment"""
+    torch_deterministic: bool = True
+    """if toggled, `torch.backends.cudnn.deterministic=False`"""
+    cuda: bool = True
+    """if toggled, cuda will be enabled by default"""
+    save_model_frequency_global_steps: Optional[int] = None
+    """Saves model after every n global steps (NOTE: Setting too low can cause many/frequent writes to storage).
+    If less than 10, will error, but note that 10 could still be too frequent in many cases."""
+    load_model_path: str = None
+    """Where to load agent from"""
+    inference: bool = False
+    """Inference only mode (needs load_model_path set)"""
+    track: bool = False
+    """if toggled, this experiment will be tracked with Weights and Biases"""
+    wandb_project_name: str = "cleanRL"
+    """the wandb's project name"""
+    wandb_entity: str = None
+    """the entity (team) of wandb's project"""
+    capture_video: bool = False
+    """whether to capture videos of the agent performances (check out `videos` folder)"""
+
+    # Algorithm specific arguments
+    env_id: str = "GodotRLEnv"
+    """the id of the environment"""
+    total_timesteps: int = 15_000_000
+    """total timesteps of the experiments"""
+    learning_rate: float = 3e-4
+    """the learning rate of the optimizer"""
+    num_envs: int = 1
+    """the number of parallel game environments [note: automatically set]"""
+    num_steps: int = 1024
+    """the number of steps to run in each environment per policy rollout"""
+    anneal_lr: bool = True
+    """Toggle learning rate annealing for policy and value networks"""
+    gamma: float = 0.99
+    """the discount factor gamma"""
+    gae_lambda: float = 0.95
+    """the lambda for the general advantage estimation"""
+    num_minibatches: int = 1
+    """the number of mini-batches"""
+    update_epochs: int = 60
+    """the K epochs to update the policy"""
+    norm_adv: bool = True
+    """Toggles advantages normalization"""
+    clip_coef: float = 0.15
+    """the surrogate clipping coefficient"""
+    clip_vloss: bool = False
+    """Toggles whether or not to use a clipped loss for the value function, as per the paper."""
+    ent_coef: float = 0.005
+    """coefficient of the entropy"""
+    vf_coef: float = 0.5
+    """coefficient of the value function"""
+    max_grad_norm: float = 0.5
+    """the maximum norm for the gradient clipping"""
+    target_kl: float = 0.0065
+    """the target KL divergence threshold"""
+
+    # to be filled in runtime
+    batch_size: int = 0
+    """the batch size (computed in runtime)"""
+    minibatch_size: int = 0
+    """the mini-batch size (computed in runtime)"""
+    num_iterations: int = 0
+    """the number of iterations (computed in runtime)"""
+
+
+def layer_init(layer, std=np.sqrt(2), bias_const=0.0):
+    torch.nn.init.orthogonal_(layer.weight, std)
+    torch.nn.init.constant_(layer.bias, bias_const)
+    return layer
+
+
+class Agent(nn.Module):
+    def __init__(self, envs):
+        super().__init__()
+        self.network = nn.Sequential(
+            layer_init(nn.Linear(np.array(envs.single_observation_space.shape).prod(), 64)),
+            nn.Tanh(),
+            layer_init(nn.Linear(64, 64)),
+            nn.Tanh(),
+        )
+        self.rnn = nn.RNN(64, 64) if args.use_vanilla_rnn else nn.GRU(64, 64)
+        for name, param in self.rnn.named_parameters():
+            if "bias" in name:
+                nn.init.constant_(param, 0)
+            elif "weight" in name:
+                nn.init.orthogonal_(param, 1.0)
+
+        self.actor = layer_init(nn.Linear(64, envs.single_action_space.n), std=0.01)
+        self.critic = layer_init(nn.Linear(64, 1), std=1)
+
+    def get_states(self, x, rnn_state, done):
+        hidden = self.network(x)
+
+        # RNN logic
+        batch_size = rnn_state.shape[1]
+        hidden = hidden.reshape((-1, batch_size, self.rnn.input_size))
+        done = done.reshape((-1, batch_size))
+        new_hidden = []
+        for h, d in zip(hidden, done):
+            h, rnn_state = self.rnn(h.unsqueeze(0), (1.0 - d).view(1, -1, 1) * rnn_state)
+            new_hidden += [h]
+        new_hidden = torch.flatten(torch.cat(new_hidden), 0, 1)
+        return new_hidden, rnn_state
+
+    def get_value(self, x, rnn_state, done):
+        hidden, _ = self.get_states(x, rnn_state, done)
+        return self.critic(hidden)
+
+    def get_action_and_value(self, x, rnn_state, done, action=None):
+        hidden, rnn_state = self.get_states(x, rnn_state, done)
+        logits = self.actor(hidden)
+        probs = Categorical(logits=logits)
+        if action is None:
+            action = probs.sample()
+        return action, probs.log_prob(action), probs.entropy(), self.critic(hidden), rnn_state
+
+
+def maybe_load_agent() -> Optional[Agent]:
+    if args.load_model_path is None:
+        return None
+    print("Loading model: " + os.path.abspath(args.load_model_path))
+    agent_state_dict = torch.load(pathlib.Path(args.load_model_path), map_location=device, weights_only=True)
+    new_agent = Agent(envs).to(device)
+    new_agent.load_state_dict(agent_state_dict)
+    new_agent.eval()
+    return new_agent
+
+
+def save_agent(filename: str = ""):
+    log_dir = writer.get_logdir()
+    path = (pathlib.Path(log_dir) / filename).with_suffix(".pt")
+    print("Saving model: " + os.path.abspath(path))
+    torch.save(agent.state_dict(), path)
+
+
+if __name__ == "__main__":
+    args = tyro.cli(Args)
+
+    if args.save_model_frequency_global_steps and args.save_model_frequency_global_steps <= 10:
+        raise ValueError("save_frequency is below 10. This would likely save too frequently.")
+
+    # env setup
+    envs = env = CleanRLGodotEnv(
+        env_path=args.env_path,
+        show_window=args.viz,
+        speedup=args.speedup,
+        seed=args.seed,
+        n_parallel=args.n_parallel,
+    )
+    args.num_envs = envs.num_envs
+    assert isinstance(envs.single_action_space, gym.spaces.Discrete), "only discrete action space is supported"
+
+    if args.inference:
+        device = torch.device("cuda" if torch.cuda.is_available() and args.cuda else "cpu")
+
+        if not args.load_model_path:
+            print("Inference requires load_model_path to be set.")
+            sys.exit(1)
+        inference_agent = maybe_load_agent()
+        if not inference_agent:
+            print("Failed to load model. Can't proceed with inference.")
+            sys.exit(1)
+
+        next_rnn_state = torch.zeros(inference_agent.rnn.num_layers, args.num_envs, inference_agent.rnn.hidden_size).to(
+            device
+        )
+        next_obs, _ = envs.reset(seed=args.seed)
+        next_obs = torch.Tensor(next_obs).to(device)
+        next_done = torch.zeros(args.num_envs).to(device)
+        for i in range(args.total_timesteps):
+            with torch.no_grad():
+                action, logprob, _, value, next_rnn_state = inference_agent.get_action_and_value(
+                    next_obs, next_rnn_state, next_done
+                )
+            next_obs, reward, terminations, truncations, infos = envs.step(action.cpu().numpy())
+            next_done = np.logical_or(terminations, truncations)
+            next_obs, next_done = torch.Tensor(next_obs).to(device), torch.Tensor(next_done).to(device)
+        sys.exit(0)
+
+    args.batch_size = int(args.num_envs * args.num_steps)
+    args.minibatch_size = int(args.batch_size // args.num_minibatches)
+    args.num_iterations = args.total_timesteps // args.batch_size
+    run_name = f"{args.env_id}__{args.exp_name}__{args.seed}__{int(time.time())}"
+    if args.track:
+        import wandb
+
+        wandb.init(
+            project=args.wandb_project_name,
+            entity=args.wandb_entity,
+            sync_tensorboard=True,
+            config=vars(args),
+            name=run_name,
+            monitor_gym=True,
+            save_code=True,
+        )
+    writer = SummaryWriter(f"runs/{run_name}")
+    writer.add_text(
+        "hyperparameters",
+        "|param|value|\n|-|-|\n%s" % ("\n".join([f"|{key}|{value}|" for key, value in vars(args).items()])),
+    )
+
+    # TRY NOT TO MODIFY: seeding
+    random.seed(args.seed)
+    np.random.seed(args.seed)
+    torch.manual_seed(args.seed)
+    torch.backends.cudnn.deterministic = args.torch_deterministic
+
+    device = torch.device("cuda" if torch.cuda.is_available() and args.cuda else "cpu")
+
+    agent = Agent(envs).to(device)
+    optimizer = optim.Adam(agent.parameters(), lr=args.learning_rate, eps=1e-5)
+
+    # ALGO Logic: Storage setup
+    obs = torch.zeros((args.num_steps, args.num_envs) + envs.single_observation_space.shape).to(device)
+    actions = torch.zeros((args.num_steps, args.num_envs) + envs.single_action_space.shape).to(device)
+    logprobs = torch.zeros((args.num_steps, args.num_envs)).to(device)
+    rewards = torch.zeros((args.num_steps, args.num_envs)).to(device)
+    dones = torch.zeros((args.num_steps, args.num_envs)).to(device)
+    values = torch.zeros((args.num_steps, args.num_envs)).to(device)
+
+    # TRY NOT TO MODIFY: start the game
+    global_step = 0
+    start_time = time.time()
+    next_obs, _ = envs.reset(seed=args.seed)
+    next_obs = torch.Tensor(next_obs).to(device)
+    next_done = torch.zeros(args.num_envs).to(device)
+    next_rnn_state = torch.zeros(agent.rnn.num_layers, args.num_envs, agent.rnn.hidden_size).to(device)
+
+    # episode reward stats, modified as Godot RL does not return this information in info (yet)
+    episode_returns = deque(maxlen=40)
+    accum_rewards = np.zeros(args.num_envs)
+
+    # tracks how many steps have elapsed since the last model saved
+    global_steps_since_save = 0
+
+    for iteration in range(1, args.num_iterations + 1):
+        # initial_rnn_state = (next_rnn_state[0].clone(), next_rnn_state[1].clone())
+        initial_rnn_state = next_rnn_state.clone()
+        # Annealing the rate if instructed to do so.
+        if args.anneal_lr:
+            frac = 1.0 - (iteration - 1.0) / args.num_iterations
+            lrnow = frac * args.learning_rate
+            optimizer.param_groups[0]["lr"] = lrnow
+
+        for step in range(0, args.num_steps):
+            save_frequency: Optional[int] = args.save_model_frequency_global_steps
+            if save_frequency:
+                if global_steps_since_save >= save_frequency:
+                    save_agent(str(global_step) + "_steps")
+                    global_steps_since_save = 0
+                global_steps_since_save += args.num_envs
+
+            global_step += args.num_envs
+            obs[step] = next_obs
+            dones[step] = next_done
+
+            # ALGO LOGIC: action logic
+            with torch.no_grad():
+                action, logprob, _, value, next_rnn_state = agent.get_action_and_value(
+                    next_obs, next_rnn_state, next_done
+                )
+                values[step] = value.flatten()
+            actions[step] = action
+            logprobs[step] = logprob
+
+            # TRY NOT TO MODIFY: execute the game and log data.
+            next_obs, reward, terminations, truncations, infos = envs.step(action.cpu().numpy())
+            next_done = np.logical_or(terminations, truncations)
+            rewards[step] = torch.tensor(reward).to(device).view(-1)
+            next_obs, next_done = torch.Tensor(next_obs).to(device), torch.Tensor(next_done).to(device)
+
+            accum_rewards += np.array(reward)
+
+            for i, d in enumerate(next_done):
+                if d:
+                    episode_returns.append(accum_rewards[i])
+                    accum_rewards[i] = 0
+
+        # bootstrap value if not done
+        with torch.no_grad():
+            next_value = agent.get_value(
+                next_obs,
+                next_rnn_state,
+                next_done,
+            ).reshape(1, -1)
+            advantages = torch.zeros_like(rewards).to(device)
+            lastgaelam = 0
+            for t in reversed(range(args.num_steps)):
+                if t == args.num_steps - 1:
+                    nextnonterminal = 1.0 - next_done
+                    nextvalues = next_value
+                else:
+                    nextnonterminal = 1.0 - dones[t + 1]
+                    nextvalues = values[t + 1]
+                delta = rewards[t] + args.gamma * nextvalues * nextnonterminal - values[t]
+                advantages[t] = lastgaelam = delta + args.gamma * args.gae_lambda * nextnonterminal * lastgaelam
+            returns = advantages + values
+
+        # flatten the batch
+        b_obs = obs.reshape((-1,) + envs.single_observation_space.shape)
+        b_logprobs = logprobs.reshape(-1)
+        b_actions = actions.reshape((-1,) + envs.single_action_space.shape)
+        b_dones = dones.reshape(-1)
+        b_advantages = advantages.reshape(-1)
+        b_returns = returns.reshape(-1)
+        b_values = values.reshape(-1)
+
+        # Optimizing the policy and value network
+        assert args.num_envs % args.num_minibatches == 0
+        envsperbatch = args.num_envs // args.num_minibatches
+        envinds = np.arange(args.num_envs)
+        flatinds = np.arange(args.batch_size).reshape(args.num_steps, args.num_envs)
+        clipfracs = []
+        for epoch in range(args.update_epochs):
+            np.random.shuffle(envinds)
+            for start in range(0, args.num_envs, envsperbatch):
+                end = start + envsperbatch
+                mbenvinds = envinds[start:end]
+                mb_inds = flatinds[:, mbenvinds].ravel()  # be really careful about the index
+
+                _, newlogprob, entropy, newvalue, _ = agent.get_action_and_value(
+                    b_obs[mb_inds],
+                    # (initial_rnn_state[0][:, mbenvinds], initial_rnn_state[1][:, mbenvinds]),
+                    initial_rnn_state[:, mbenvinds],
+                    b_dones[mb_inds],
+                    b_actions.long()[mb_inds],
+                )
+                logratio = newlogprob - b_logprobs[mb_inds]
+                ratio = logratio.exp()
+
+                with torch.no_grad():
+                    # calculate approx_kl http://joschu.net/blog/kl-approx.html
+                    old_approx_kl = (-logratio).mean()
+                    approx_kl = ((ratio - 1) - logratio).mean()
+                    clipfracs += [((ratio - 1.0).abs() > args.clip_coef).float().mean().item()]
+
+                mb_advantages = b_advantages[mb_inds]
+                if args.norm_adv:
+                    mb_advantages = (mb_advantages - mb_advantages.mean()) / (mb_advantages.std() + 1e-8)
+
+                # Policy loss
+                pg_loss1 = -mb_advantages * ratio
+                pg_loss2 = -mb_advantages * torch.clamp(ratio, 1 - args.clip_coef, 1 + args.clip_coef)
+                pg_loss = torch.max(pg_loss1, pg_loss2).mean()
+
+                # Value loss
+                newvalue = newvalue.view(-1)
+                if args.clip_vloss:
+                    v_loss_unclipped = (newvalue - b_returns[mb_inds]) ** 2
+                    v_clipped = b_values[mb_inds] + torch.clamp(
+                        newvalue - b_values[mb_inds],
+                        -args.clip_coef,
+                        args.clip_coef,
+                    )
+                    v_loss_clipped = (v_clipped - b_returns[mb_inds]) ** 2
+                    v_loss_max = torch.max(v_loss_unclipped, v_loss_clipped)
+                    v_loss = 0.5 * v_loss_max.mean()
+                else:
+                    v_loss = 0.5 * ((newvalue - b_returns[mb_inds]) ** 2).mean()
+
+                entropy_loss = entropy.mean()
+                loss = pg_loss - args.ent_coef * entropy_loss + v_loss * args.vf_coef
+
+                optimizer.zero_grad()
+                loss.backward()
+                nn.utils.clip_grad_norm_(agent.parameters(), args.max_grad_norm)
+                optimizer.step()
+
+            if args.target_kl is not None and approx_kl > args.target_kl:
+                break
+
+        y_pred, y_true = b_values.cpu().numpy(), b_returns.cpu().numpy()
+        var_y = np.var(y_true)
+        explained_var = np.nan if var_y == 0 else 1 - np.var(y_true - y_pred) / var_y
+
+        # TRY NOT TO MODIFY: record rewards for plotting purposes
+        writer.add_scalar("charts/learning_rate", optimizer.param_groups[0]["lr"], global_step)
+        writer.add_scalar("losses/value_loss", v_loss.item(), global_step)
+        writer.add_scalar("losses/policy_loss", pg_loss.item(), global_step)
+        writer.add_scalar("losses/entropy", entropy_loss.item(), global_step)
+        writer.add_scalar("losses/old_approx_kl", old_approx_kl.item(), global_step)
+        writer.add_scalar("losses/approx_kl", approx_kl.item(), global_step)
+        writer.add_scalar("losses/clipfrac", np.mean(clipfracs), global_step)
+        writer.add_scalar("losses/explained_variance", explained_var, global_step)
+        print("SPS:", int(global_step / (time.time() - start_time)))
+        print(f"Approx KL: {approx_kl.item():.4f}")
+        if len(episode_returns) > 0:
+            print(
+                "Returns (Last 40 episodes) Mean:",
+                np.mean(np.array(episode_returns)),
+                "Max:",
+                np.max(np.array(episode_returns)),
+                "Min:",
+                np.min(np.array(episode_returns)),
+            )
+            writer.add_scalar("charts/episodic_return", np.mean(np.array(episode_returns)), global_step)
+        print("---")
+        writer.add_scalar("charts/SPS", int(global_step / (time.time() - start_time)), global_step)
+
+    envs.close()
+    writer.close()
diff --git a/examples/clean_rl_gru_ppo/readme.md b/examples/clean_rl_gru_ppo/readme.md
new file mode 100644
index 00000000..dd7e7008
--- /dev/null
+++ b/examples/clean_rl_gru_ppo/readme.md
@@ -0,0 +1,26 @@
+### CleanRL PPO GRU Discrete Actions example
+
+This example is a modification of [CleanRL PPO Atari LSTM](https://github.com/vwxyzjn/cleanrl/blob/master/cleanrl/ppo_atari_lstm.py),
+it's adjusted to work with GDRL and vector obs, along with adding inference, changing the default params, and other modifications.
+
+You may need to install tyro using `pip install tyro`. If you get an error while running the script: `ModuleNotFoundError: No module named 'tyro'`, install it.
+
+## Observations:
+- Works with vector observations.
+
+## Actions:
+- Accepts a single discrete action space.
+
+## CL arguments unique to this example:
+### RNN settings:
+By default, uses GRU. It can use vanilla RNN instead if you use the CL argument `--use_vanilla_rnn`
+
+### Checkpoint saving:
+Example: Save checkpoint every 500_000 steps: `--save_model_frequency_global_steps=500_000`.
+If you don't set this argument, the model will not be saved, only the logs.
+The checkpoints will be saved inside the `runs` folder in a different folder for each run, you will see the full path displayed in console when a checkpoint is saved.
+
+### Inference:
+Example use: `--load_model_path=path_to_saved_file.pt --inference` (set the true path to a checkpoint).
+
+Other CL args should be similar to those described in https://github.com/edbeeching/godot_rl_agents/blob/main/docs/ADV_CLEAN_RL.md (but there is no onnx export/inference currently implemented for this example).