DqnTrainer.py

import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
import random
from collections import namedtuple, deque
import torch.optim as optim
from unityagents import UnityEnvironment
import pickle

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 = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
if torch.cuda.is_available():
    print("Using GPU :-)")
    device = torch.device("cuda:0")
else:
    print("Using CPU :-(")
    device = torch.device("cpu")


class QNetwork(nn.Module):
    """Actor (Policy) Model."""

    def __init__(self, state_size, action_size, seed):
        """Initialize parameters and build model.
        Params
        ======
            state_size (int): Dimension of each state
            action_size (int): Dimension of each action
            seed (int): Random seed
        """
        super(QNetwork, self).__init__()
        self.seed = torch.manual_seed(seed)

        fc1_units = 64
        self.fc1 = nn.Linear(state_size, fc1_units)
        self.bn1 = nn.BatchNorm1d(fc1_units)

        fc2_units = 64
        self.fc2 = nn.Linear(fc1_units, fc2_units)
        self.bn2 = nn.BatchNorm1d(fc2_units)

        self.fc3 = nn.Linear(fc2_units, action_size)

    def forward(self, state):
        """Build a network that maps state -> action values."""

        state = F.relu(self.bn1(self.fc1(state)))
        state = F.relu(self.bn2(self.fc2(state)))
        state = self.fc3(state)

        return state


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 _get_action_values(self, state, network=None):
        if network is None:
            network = self.qnetwork_local

        state = torch.from_numpy(state).float().unsqueeze(0).to(device)
        network.eval()
        with torch.no_grad():
            action_values = network(state)
            network.train()

        return action_values

    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
        """
        action_values = self._get_action_values(state)

        # 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.Tensor]): tuple of (s, a, r, s', done) tuples
            gamma (float): discount factor
        """
        states, actions, rewards, next_states, dones = experiences

        Q_targets_next = self.qnetwork_target(next_states).detach().max(1)[0].unsqueeze(1)
        Q_targets = rewards + (gamma * Q_targets_next * (1 - dones))

        Q_local = self.qnetwork_local(states).gather(1, actions)

        loss = F.mse_loss(Q_targets, Q_local)

        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)


def dqn(env, agent, brain_name, n_episodes=2000, max_t=1000, eps_start=1.0, eps_end=0.01, eps_decay=0.995):
    """Deep Q-Learning.

    Params
    ======
        n_episodes (int): maximum number of training episodes
        max_t (int): maximum number of timesteps per episode
        eps_start (float): starting value of epsilon, for epsilon-greedy action selection
        eps_end (float): minimum value of epsilon
        eps_decay (float): multiplicative factor (per episode) for decreasing epsilon
    """
    scores = []  # list containing scores from each episode
    scores_window = deque(maxlen=100)  # last 100 scores
    eps = eps_start  # initialize epsilon
    for i_episode in range(1, n_episodes + 1):
        state = env.reset(train_mode=True)[brain_name].vector_observations[0]
        score = 0
        for t in range(max_t):
            action = agent.act(state, eps)
            env_info = env.step(action)[brain_name]

            next_state = env_info.vector_observations[0]
            reward = env_info.rewards[0]
            done = env_info.local_done[0]

            agent.step(state, action, reward, next_state, done)
            state = next_state
            score += reward
            if done:
                break
        scores_window.append(score)  # save most recent score
        scores.append(score)  # save most recent score
        eps = max(eps_end, eps_decay * eps)  # decrease epsilon
        print('\rEpisode {}\tAverage Score: {:.2f}'.format(i_episode, np.mean(scores_window)), end="")
        if i_episode % 100 == 0:
            print('\rEpisode {}\tAverage Score: {:.2f}'.format(i_episode, np.mean(scores_window)))
        if np.mean(scores_window) >= 13.0:
            print('\nEnvironment solved in {:d} episodes!\tAverage Score: {:.2f}'.format(i_episode - 100,
                                                                                         np.mean(scores_window)))
            torch.save(agent.qnetwork_local.state_dict(), 'checkpoint.pth')
            return scores, True
    return scores, False


if __name__ == "__main__":
    import argparse
    parser = argparse.ArgumentParser()
    parser.add_argument("--unity_filename", default="Banana_Linux/Banana.x86_64")
    parser.add_argument("--model_filename", default="banana.torch")
    parser.add_argument("--scores_filename", default="banana_scores.pkl")
    parser.add_argument("--seed", default=42)
    parser.add_argument("--max_episodes", default=2000)
    args = parser.parse_args()

    env_ = UnityEnvironment(file_name=args.unity_filename, no_graphics=True)

    # get the default brain
    brain_name_ = env_.brain_names[0]
    brain_ = env_.brains[brain_name_]

    env_info = env_.reset(train_mode=True)[brain_name_]

    action_size_ = brain_.vector_action_space_size
    state_ = env_info.vector_observations[0]
    state_size_ = len(state_)

    agent_ = Agent(state_size=state_size_, action_size=action_size_, seed=args.seed)

    scores_, solved_ = dqn(env=env_, agent=agent_, brain_name=brain_name_, n_episodes=args.max_episodes)

    if solved_:
        torch.save(agent_.qnetwork_local.state_dict(), open(args.model_filename, "wb"))
        print("Network saved to", args.model_filename)
        pickle.dump(scores_, open(args.scores_filename, "wb"))
        print("Scores saved to ", args.scores_filename)
    else:
        print("Failed to solve, network not saved")

    env_.close()