ddpg_agent.py

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

from model import Actor, Critic
from memory import ReplayBuffer
from hyperparameters  import *

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


GAMMA = 0.995 #0.99     # discount factor
TAU = 1e-3              # for soft update of target parameters
LR_ACTOR = 1e-4         # learning rate of the actor 
LR_CRITIC = 1e-3        # learning rate of the critic
WEIGHT_DECAY = 0.       # L2 weight decay

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

class Agent():
    """DDPG Agent : Interacts with and learns from the environment."""
    
    def __init__(self, state_size, action_size, random_seed, num_agents=1):
        """Initialize a DDPG Agent object.
        
        Params
        ======
            state_size (int): dimension of each state
            action_size (int): dimension of each action
            random_seed (int): random seed
            num_agents (int) : Number of agents (1 for DDPG, 2+ for MADDPG -> Will affect the critic)
        """
        self.state_size = state_size
        self.action_size = action_size
        self.seed = random.seed(random_seed)
        self.num_agents = num_agents

        # Actor Network (w/ Target Network)
        self.actor_local = Actor(state_size, action_size, random_seed).to(device)
        self.actor_target = Actor(state_size, action_size, random_seed).to(device)
        self.actor_optimizer = optim.Adam(self.actor_local.parameters(), lr=LR_ACTOR)
        
        # Make sure the Actor Target Network has the same weight values as the Local Network
        for target, local in zip(self.actor_target.parameters(), self.actor_local.parameters()):
            target.data.copy_(local.data)

        # Critic Network (w/ Target Network)
        # Note : in MADDPG, critics have access to all agents obeservations and actions
        self.critic_local = Critic(state_size*num_agents, action_size*num_agents, random_seed).to(device)
        self.critic_target = Critic(state_size*num_agents, action_size*num_agents, random_seed).to(device)
        self.critic_optimizer = optim.Adam(self.critic_local.parameters(), lr=LR_CRITIC, weight_decay=WEIGHT_DECAY)
        
        # Make sure the Critic Target Network has the same weight values as the Local Network
        for target, local in zip(self.critic_target.parameters(), self.critic_local.parameters()):
            target.data.copy_(local.data)

        # Noise process
        self.noise = OUNoise(action_size, random_seed)

        # Replay memory : in MADDPG, the ReplayBuffer is common to all agents
        #self.memory = ReplayBuffer(action_size, BUFFER_SIZE, BATCH_SIZE, random_seed)
        
    
    def step(self, state, action, reward, next_state, done):
        ##TODO : not used with MADDPG ..
        """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) > BATCH_SIZE:
            experiences = self.memory.sample()
            self.learn(experiences, GAMMA)
            

    def act(self, state, noise=0.0):
        """Returns actions for given state as per current policy."""
        state = torch.from_numpy(state).float().to(device)
        self.actor_local.eval()
        with torch.no_grad():
            action = self.actor_local(state).cpu().data.numpy()
        self.actor_local.train()
        if ADD_OU_NOISE:
            action += self.noise.sample() * noise 
        return np.clip(action, -1, 1)

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

    def learn(self, experiences, gamma):
        ### Used only for DDPG (use madddpg.maddpg_learn() for MADDPG)
        """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
        """
        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)
        Q_targets = rewards + (gamma * Q_targets_next * (1 - dones))
        # Compute critic loss
        Q_expected = self.critic_local(states, actions)
        critic_loss = F.mse_loss(Q_expected, Q_targets)
        # Minimize the loss
        self.critic_optimizer.zero_grad()
        critic_loss.backward()
        self.critic_optimizer.step()

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

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

        Params
        ======
            size (int) : size of action space
            target_model: PyTorch model (weights will be copied to)
            mu (float) :  Ornstein-Uhlenbeck noise parameter
            theta (float) :  Ornstein-Uhlenbeck noise parameter
            sigma (flmoat) : Ornstein-Uhlenbeck noise parameter 
        """
    def __init__(self, size, seed, mu=MU, theta=THETA, sigma=SIGMA):
        """Initialize parameters and noise process."""
        self.size=size
        self.mu = mu * np.ones(size)
        self.theta = theta
        self.sigma = sigma
        self.seed = 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))])
        dx = self.theta * (self.mu - x) + self.sigma * np.random.randn(self.size) # use normal distribution
        self.state = x + dx
        return self.state