dqn_agent.py

"""

This module implements a DQN Agent.

The Idea and most of the code comes from the Deep Reinforcement Learning
Nanodegree from Udacity.

Author: Kai Waelti

"""

from collections import namedtuple, deque
import random
import numpy as np
import torch
import torch.optim as optim
from torch import device
from model import QNetwork


BUFFER_SIZE = int(1e5)  # replay buffer size
BATCH_SIZE = 64         # minibatch size
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 = device("cuda:0" if torch.cuda.is_available() else "cpu")


class Agent():
    """Interacts with and learns from the environment."""

    def __init__(self, state_size, action_size, seed):
        """Initialize an Agent object.

        Params
        ======
            state_size (int): dimension of each state
            action_size (int): dimension of each action
            seed (int): random seed
        """
        self.state_size = state_size
        self.action_size = action_size
        self.seed = random.seed(seed)

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

        # Replay memory
        self.memory = ReplayBuffer(action_size, BUFFER_SIZE, BATCH_SIZE, seed)
        # Initialize time step (for updating every UPDATE_EVERY steps)
        self.t_step = 0


    def step(self, state, action, reward, next_state, done):
        # Save experience in replay memory
        self.memory.add(state, action, reward, next_state, done)

        # Learn every UPDATE_EVERY time steps.
        self.t_step = (self.t_step + 1) % UPDATE_EVERY
        if self.t_step == 0:
            # If enough samples are available in memory,
            # get random subset and learn
            if len(self.memory) > BATCH_SIZE:
                experiences = self.memory.sample()
                self.learn(experiences, GAMMA)

    def act(self, state, eps=0.):
        """Returns actions for given state as per current policy.

        Params
        ======
            state (array_like): current state
            eps (float): epsilon, for epsilon-greedy action selection
        """
        state = torch.from_numpy(state).float().unsqueeze(0).to(DEVICE)
        # Notify all layers to work in eval mode
        self.qnetwork_local.eval()
        # Deactivate autograd engine -> reduces memory & speeds up computation
        with torch.no_grad():
            action_values = self.qnetwork_local(state)
        # Re-enable train mode in all layers
        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))

    def learn(self, experiences, gamma):
        """Update value parameters using given batch of experience tuples.

        Params
        ======
            experiences (Tuple[torch.Variable]): tuple of (s, a, r, s', done) tuples
            gamma (float): discount factor
        """
        states, actions, rewards, next_states, dones = experiences

        # Compute and minimize the loss
        criterion = torch.nn.MSELoss()

        ## Move input and label tensors to correct device
        self.qnetwork_local.to(DEVICE)
        self.qnetwork_target.to(DEVICE)
        inputs = next_states.to(DEVICE)

        ## Select max predicted Q value for next state using the target model
        with torch.no_grad():
            next_target = self.qnetwork_target(inputs)
            next_q_target = next_target.max(1)[0].unsqueeze(1)
        ## Calculate q targets
        target_q = rewards + (gamma * next_q_target * (1 - dones))

        ## Use local model to get the expected Q value
        expected_q = self.qnetwork_local(states).gather(1, actions)

        ## Compute and minimize the loss
        loss = criterion(expected_q, target_q)
        self.optimizer.zero_grad()
        loss.backward()
        self.optimizer.step()

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

    def soft_update(self, local_model, target_model, tau):
        """Soft update model parameters.
        θ_target = τ*θ_local + (1 - τ)*θ_target

        Params
        ======
            local_model (PyTorch model): weights will be copied from
            target_model (PyTorch model): weights will be copied to
            tau (float): interpolation parameter
        """
        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)


class ReplayBuffer:
    """Fixed-size buffer to store experience tuples."""

    def __init__(self, action_size, buffer_size, batch_size, seed):
        """Initialize a ReplayBuffer object.

        Params
        ======
            action_size (int): dimension of each action
            buffer_size (int): maximum size of buffer
            batch_size (int): size of each training batch
            seed (int): random seed
        """
        self.action_size = action_size
        self.memory = deque(maxlen=buffer_size)
        self.batch_size = batch_size
        self.experience = namedtuple("Experience", field_names=["state", "action", "reward", "next_state", "done"])
        self.seed = random.seed(seed)

    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])).long().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]).astype(np.uint8)).float().to(DEVICE)

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

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