ddpg.py

import numpy as np
import random
import dill
import copy

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

from collections import namedtuple, deque

from model import Actor, Critic

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

class OUNoise:
    """Ornstein-Uhlenbeck process."""

    def __init__(self, size, seed, mu=0., theta=0.15, sigma=0.05):
        """Initialize parameters and noise process."""
        self.mu = mu * np.ones(size)
        self.theta = theta
        self.sigma = sigma
        random.seed(seed)
        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 ReplayBuffer:
    
    def __init__(self, action_size, buffer_size, batch_size, seed):       
        self.action_size = action_size
        self.memory = deque(maxlen=buffer_size)  # internal memory (deque)
        self.batch_size = batch_size
        self.experience = namedtuple("Experience", field_names=["state", "action", "reward", "next_state", "done"])
        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])).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]).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 save(self, fileName):
        with open(fileName, 'wb') as file:
            dill.dump(self.memory, file)
    
    def load(self, fileName):
        with open(fileName, 'rb') as file:
            self.memory = dill.load(file)



class DDPG():

    def __init__(self, state_size, action_size, random_seed=23,
                 fc1_units=96, fc2_units=96, epsilon=1.0, lr_actor=1e-3,
                 lr_critic=1e-3, weight_decay=0):
        self.state_size = state_size
        self.action_size = action_size 
        self.random_seed = random_seed
        self.fc1_units = fc1_units
        self.fc2_units = fc2_units
        self.state_size = state_size
        self.action_size = action_size        
        self.epsilon = epsilon
        self.lr_actor = lr_actor
        self.lr_critic = lr_critic
        self.weight_decay = weight_decay
        self.noise = OUNoise(action_size, random_seed)
        random.seed(random_seed)
        self.recreate()
        
        

    def recreate(self):
        self.actor = Actor(self.state_size, self.action_size, self.random_seed,
                           fc1_units=self.fc1_units, fc2_units=self.fc2_units).to(device)
        self.actor_target = Actor(self.state_size, self.action_size, self.random_seed,
                                  fc1_units=self.fc1_units, fc2_units=self.fc2_units).to(device)
        self.actor_optimizer = optim.Adam(self.actor.parameters(), lr=self.lr_actor)

        self.critic = Critic(self.state_size, self.action_size, self.random_seed,
                             fc1_units=self.fc1_units, fc2_units=self.fc2_units).to(device)
        self.critic_target = Critic(self.state_size, self.action_size, self.random_seed,
                                    fc1_units=self.fc1_units, fc2_units=self.fc2_units).to(device)
        self.critic_optimizer = optim.Adam(self.critic.parameters(), lr=self.lr_critic, 
                                           weight_decay=self.weight_decay)
        
        self.hard_copy(self.actor_target, self.actor)
        self.hard_copy(self.critic_target, self.critic)

    def act(self, state, add_noise=True):
        state = torch.from_numpy(state).float().to(device)

        self.actor.eval()
        with torch.no_grad():
            action = self.actor(state).cpu().data.numpy()
        self.actor.train()

        if add_noise:
            action += self.epsilon * self.noise.sample()

        return action

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

    def learn(self, experiences, gamma, tau=1e-3, epsilon_decay=1e-6):        
        states, actions, rewards, next_states, dones = experiences
        
        # 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)
        Q_targets = rewards + (gamma * Q_targets_next * (1 - dones))
        Q_expected = self.critic(states, actions)
                
    # Critic update        
        critic_loss = F.mse_loss(Q_expected, Q_targets)
        self.critic_optimizer.zero_grad()
        critic_loss.backward()
        torch.nn.utils.clip_grad_norm_(self.critic.parameters(), 1)
        self.critic_optimizer.step()

    # Actor update 
        actions_pred = self.actor(states)
        actor_loss = -self.critic(states, actions_pred).mean()
        self.actor_optimizer.zero_grad()
        actor_loss.backward()
        self.actor_optimizer.step()

   # Targets update
        self.soft_update(self.critic, self.critic_target, tau)
        self.soft_update(self.actor, self.actor_target, tau)

   #  Noise update
        self.epsilon -= epsilon_decay
        self.noise.reset()

    def soft_update(self, local_model, target_model, tau):        
        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 hard_copy(self, target, source):
        for target_param, param in zip(target.parameters(), source.parameters()):
            target_param.data.copy_(param.data)
    
    def save(self, path):
        torch.save(self.actor.state_dict(), 
                   path+ str(self.fc1_units)+'_'+str(self.fc2_units) + '_actor.pth')
        torch.save(self.critic.state_dict(),
                   path+ str(self.fc1_units)+'_'+str(self.fc2_units) + '_critic.pth')
    
    def load(self, actor_file, critic_file):
        self.actor.load_state_dict(torch.load(actor_file))
        self.critic.load_state_dict(torch.load(critic_file))
        self.hard_copy(self.actor_target, self.actor)
        self.hard_copy(self.critic_target, self.critic)