ddpg_agent.py

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

from model import Actor, Critic, ICM

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

class Agent():
    """Interacts with and learns from the environment."""
    
    def __init__(self, config):
        """Initialize an Agent object.
        
        Params
        ======
            state_size (int): dimension of each state
            action_size (int): dimension of each action
        """

        state_size = config.get('state_size')
        action_size = config.get('action_size')

        self.tau = config.get('tau', 1e-2)
        self.batch_size = config.get('batch_size', 128)
        self.gamma = config.get('gamma', 0.99)

        # Actor Network (w/ Target Network)
        self.actor_local = Actor(state_size, action_size)
        self.actor_target = Actor(state_size, action_size)
        self.actor_optimizer = optim.Adam(self.actor_local.parameters(), lr=config.get('lr_actor', 1e-3), weight_decay=0)

        # Critic Network (w/ Target Network)
        self.critic_local = Critic(state_size, action_size)
        self.critic_target = Critic(state_size, action_size)
        self.critic_optimizer = optim.Adam(self.critic_local.parameters(), lr=config.get('lr_critic', 1e-3), weight_decay=0)

        # Modules
        self.noise = config.get('noise', None)
        self.icm = config.get('icm', None)
        self.memory = config.get('memory', None)
    
    def step(self, state, action, reward, next_state, done):
        """Save experience in replay memory, and use random sample from buffer to learn."""
        # Save experience / reward
        self.memory.add(state, action, reward, next_state, done)

        # Learn, if enough samples are available in memory
        if len(self.memory) > self.batch_size:
            experiences = self.memory.sample(self.batch_size)
            self.learn(experiences)

    def act(self, state, add_noise=False, noise_scale=1.0):
        """Returns actions for given state as per current policy."""
        state = torch.from_numpy(state).float()
        self.actor_local.eval()
        with torch.no_grad():
            action = self.actor_local(state).cpu().data.numpy()
        self.actor_local.train()
        if add_noise and self.noise:
            action += noise_scale * self.noise.sample()
        return np.clip(action, -1, 1)

    def reset(self):
        if self.noise:
            self.noise.reset()

    def learn(self, experiences):
        """Update policy and value parameters using given batch of experience tuples.
        Q_targets = r + γ * critic_target(next_state, actor_target(next_state))
        where:
            actor_target(state) -> action
            critic_target(state, action) -> Q-value

        Params
        ======
            experiences (Tuple[torch.Tensor]): tuple of (s, a, r, s', done) tuples 
            gamma (float): discount factor
        """

        if hasattr(self.memory, 'priorities'):
            states, actions, rewards, next_states, dones, indices, weights = experiences
        else:
            states, actions, rewards, next_states, dones = experiences

        # ---------------------------- update critic ---------------------------- #
        # Get predicted next-state actions and Q values from target models
        actions_next = self.actor_target(next_states)
        Q_targets_next = self.critic_target(next_states, actions_next)
        # Compute Q targets for current states (y_i)
        if self.icm:
            rewards += self.icm.surprise(states, actions, next_states)

        Q_targets = rewards + (self.gamma * Q_targets_next * (1 - dones))

        # critic loss
        Q_expected = self.critic_local(states, actions)

        if hasattr(self.memory, 'priorities'):
            critic_loss = (Q_targets - Q_expected).pow(2) * weights
            priorities = critic_loss + 1e-5
            self.memory.update_priorities(indices, priorities)
            critic_loss = critic_loss.mean()
        else:
            critic_loss = F.mse_loss(Q_targets, Q_expected)
        
        self.critic_optimizer.zero_grad()
        critic_loss.backward()
        self.critic_optimizer.step()

        # ---------------------------- update actor ---------------------------- #
        # actor loss
        actions_pred = self.actor_local(states)
        actor_loss = -self.critic_local(states, actions_pred).mean()
        self.actor_optimizer.zero_grad()
        actor_loss.backward()
        
        self.actor_optimizer.step()

        # ----------------------- update target networks ----------------------- #
        self.soft_update(self.critic_local, self.critic_target, self.tau)
        self.soft_update(self.actor_local, self.actor_target, self.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 OUNoise:
    """Ornstein-Uhlenbeck process."""

    def __init__(self, size, mu=0., theta=0.15, sigma=0.2):
        """Initialize parameters and noise process."""
        self.mu = mu * np.ones(size)
        self.theta = theta
        self.sigma = sigma
        self.reset()

    def reset(self):
        """Reset the internal state (= noise) to mean (mu)."""
        self.state = copy.copy(self.mu)

    def sample(self):
        """Update internal state and return it as a noise sample."""
        x = self.state
        dx = self.theta * (self.mu - x) + self.sigma * np.array([random.random() for i in range(len(x))])
        self.state = x + dx
        return self.state

class SimpleNoise:

    def __init__(self, size):
        self.size = size

    def sample(self):
        return np.random.randn(self.size)
    
    def reset(self):
        pass

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

    def __init__(self, buffer_size):
        """Initialize a ReplayBuffer object.
        Params
        ======
            buffer_size (int): maximum size of buffer
        """
        self.memory = deque(maxlen=int(buffer_size))  # internal memory (deque)
        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, batch_size):
        """Randomly sample a batch of experiences from memory.
        Params
        ======
            batch_size (int): size of each training batch
        """
        experiences = random.sample(self.memory, k=batch_size)

        states = torch.from_numpy(np.vstack([e.state for e in experiences if e is not None])).float()
        actions = torch.from_numpy(np.vstack([e.action for e in experiences if e is not None])).float()
        rewards = torch.from_numpy(np.vstack([e.reward for e in experiences if e is not None])).float()
        next_states = torch.from_numpy(np.vstack([e.next_state for e in experiences if e is not None])).float()
        dones = torch.from_numpy(np.vstack([e.done for e in experiences if e is not None]).astype(np.uint8)).float()

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

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

class NaivePrioritizedBuffer():
    def __init__(self, capacity, prob_alpha=0.6):
        self.prob_alpha = prob_alpha
        self.capacity   = capacity
        self.buffer     = []
        self.pos        = 0
        self.priorities = np.zeros((capacity,), dtype=np.float32)
    
    def add(self, state, action, reward, next_state, done):
        assert state.ndim == next_state.ndim
        state      = np.expand_dims(state, 0)
        next_state = np.expand_dims(next_state, 0)
        
        max_prio = self.priorities.max() if self.buffer else 1.0
        
        if len(self.buffer) < self.capacity:
            self.buffer.append((state, action, reward, next_state, done))
        else:
            self.buffer[self.pos] = (state, action, reward, next_state, done)
        
        self.priorities[self.pos] = max_prio
        self.pos = (self.pos + 1) % self.capacity
    
    def sample(self, batch_size, beta=0.4):
        if len(self.buffer) == self.capacity:
            prios = self.priorities
        else:
            prios = self.priorities[:self.pos]
        
        probs  = prios ** self.prob_alpha
        probs /= probs.sum()
        
        indices = np.random.choice(len(self.buffer), batch_size, p=probs)
        samples = [self.buffer[idx] for idx in indices]
        
        total    = len(self.buffer)
        weights  = (total * probs[indices]) ** (-beta)
        weights /= weights.max()
        weights  = np.array(weights, dtype=np.float32)
        

        batch = [x for x in zip(*samples)]

        states      = np.concatenate(batch[0])
        actions     = batch[1]
        rewards     = batch[2]
        next_states = np.concatenate(batch[3])
        dones       = batch[4]

        states = torch.from_numpy(np.vstack(states)).float()
        actions = torch.from_numpy(np.vstack(actions)).float()
        rewards = torch.from_numpy(np.vstack(rewards)).float()
        next_states = torch.from_numpy(np.vstack(next_states)).float()
        dones = torch.from_numpy(np.vstack(dones).astype(np.uint8)).float()
        weights = torch.from_numpy(np.vstack(weights).astype(np.uint8)).float()

        return states, actions, rewards, next_states, dones, indices, weights
    
    def update_priorities(self, batch_indices, batch_priorities):
        for idx, prio in zip(batch_indices, batch_priorities):
            self.priorities[idx] = prio

    def __len__(self):
        return len(self.buffer)