dqn_agent.py

import numpy as np
import random
from collections import namedtuple, deque

from model import QNetwork

import torch
import torch.nn.functional as F
import torch.optim as optim

# Initialize hyperparameters
BUFFER_SIZE = int(1e5)  # size of the replay buffer
BATCH_SIZE = 64        # minibatch size for sampling
GAMMA = 0.99           # discount factor
TAU = 1e-3             # for soft update of target parameters
LR = 5e-4              # learning rate
UPDATE_EVERY = 4       # how often to update the network

device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")

class ReplayBuffer:
    def __init__(self, action_size, buffer_size, batch_size):
        self.action_size = action_size
        self.memory = deque(maxlen=buffer_size)  # initialize replay buffer
        self.batch_size = batch_size
        self.experience = namedtuple("Experience", field_names=["state", "action", "reward", "next_state", "done"])

    def add(self, state, action, reward, next_state, done):
        """Add a new experience to memory."""
        e = self.experience(state, action, reward, next_state, done)
        self.memory.append(e)

    def sample(self):
        """Randomly sample a batch of experiences from memory."""
        experiences = random.sample(self.memory, k=self.batch_size)
        
        states = torch.from_numpy(np.vstack([e.state for e in experiences if e is not None])).float().to(device)
        actions = torch.from_numpy(np.vstack([e.action for e in experiences if e is not None])).float().to(device)
        rewards = torch.from_numpy(np.vstack([e.reward for e in experiences if e is not None])).float().to(device)
        next_states = torch.from_numpy(np.vstack([e.next_state for e in experiences if e is not None])).float().to(device)
        dones = torch.from_numpy(np.vstack([e.done for e in experiences if e is not None])).float().to(device)

        return (states, actions, rewards, next_states, dones)

    def __len__(self):
        """Return the current size of internal memory."""
        return len(self.memory)

class Agent:
    def __init__(self, state_size, action_size):
        self.state_size = state_size
        self.action_size = action_size

        # Q-Network
        self.qnetwork_local = QNetwork(state_size, action_size).to(device)
        self.qnetwork_target = QNetwork(state_size, action_size).to(device)
        self.optimizer = optim.Adam(self.qnetwork_local.parameters(), lr=LR)

        # Replay memory
        self.memory = ReplayBuffer(action_size, buffer_size=BUFFER_SIZE, batch_size=BATCH_SIZE)
        self.t_step = 0  # initialize time step
        self.balance = []  # initialize balance

    def step(self, state, action, reward, next_state, done):
        """Save experience in replay memory, and use random sample from buffer to learn."""
        
        self.memory.add(state, action, reward, next_state, done)  # Add experience to memory

        # Learn every UPDATE_EVERY time steps.
        self.t_step = (self.t_step + 1) % UPDATE_EVERY
        if self.t_step == 0:
            if len(self.memory) > BATCH_SIZE:
                experiences = self.memory.sample()
                self.learn(experiences, GAMMA)

    def learn(self, experiences, gamma):
        """Update value parameters using given batch of experience tuples."""
        states, actions, rewards, next_states, dones = experiences

        # Get max predicted Q values (for next states) from target model
        Q_targets_next = self.qnetwork_target(next_states).detach().max(1)[0].unsqueeze(1)
        Q_targets = rewards + (gamma * Q_targets_next * (1 - dones))

        # Get expected Q values from local model
        Q_expected = self.qnetwork_local(states).gather(1, actions.long())

        # Compute loss
        loss = F.mse_loss(Q_expected, Q_targets)
        self.optimizer.zero_grad()
        loss.backward()
        self.optimizer.step()

        # ------------------- update target network ------------------- #
        self.soft_update(self.qnetwork_local, self.qnetwork_target, tau=TAU)

    def soft_update(self, local_model, target_model, tau):
        """Soft update model parameters.
        θ_target = τ*θ_local + (1 - τ)*θ_target
        """
        for target_param, local_param in zip(target_model.parameters(), local_model.parameters()):
            target_param.data.copy_(tau * local_param.data + (1.0 - tau) * target_param.data)

    def act(self, state, eps=0.):
        """Returns actions for given state as per current policy."""
        state = torch.from_numpy(state).float().unsqueeze(0).to(device)
        self.qnetwork_local.eval()
        with torch.no_grad():
            action_values = self.qnetwork_local(state)
        self.qnetwork_local.train()

        # Epsilon-greedy action selection
        if random.random() > eps:
            return np.argmax(action_values.cpu().data.numpy())
        else:
            return random.choice(np.arange(self.action_size))