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ma_ddpg_agent.py
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ma_ddpg_agent.py
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import numpy as np
import random
import copy
import time
from collections import namedtuple, deque
from model import Actor, Critic
import torch
import torch.nn.functional as F
import torch.optim as optim
BUFFER_SIZE = int(1e6) # replay buffer size
BATCH_SIZE = 128 # minibatch size
GAMMA = 0.99 # discount factor
TAU = 8e-3 # for soft update of target parameters
LR_ACTOR = 1e-3 # learning rate of the actor
LR_CRITIC = 1e-3 # learning rate of the critic
WEIGHT_DECAY = 0 # L2 weight decay
LEARN_EVERY = 1 # learning timestep interval
LEARN_NUM = 5 # number of learning passes
OU_SIGMA = 0.2 # Ornstein-Uhlenbeck noise parameter, volatility
OU_THETA = 0.15 # Ornstein-Uhlenbeck noise parameter, speed of mean reversion
device = torch.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, random_seed, num_agents):
"""Initialize an Agent object.
Params
======
state_size (int): dimension of each state
action_size (int): dimension of each action
num_agents (int): number of agents
random_seed (int): random seed
"""
self.state_size = state_size
self.action_size = action_size
self.seed = random.seed(random_seed)
self.num_agents = num_agents
self.eps = 5.0
self.eps_decay = 1/(300 * LEARN_NUM) # set decay rate based on epsilon end target
self.timestep = 0
# 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)
# Critic Network (w/ Target Network)
self.critic_local = Critic(state_size, action_size, random_seed).to(device)
self.critic_target = Critic(state_size, action_size, random_seed).to(device)
self.critic_optimizer = optim.Adam(self.critic_local.parameters(), lr=LR_CRITIC, weight_decay=WEIGHT_DECAY)
# Noise process
self.noise = OUNoise((num_agents, action_size), random_seed)
# Replay memory
self.memory = ReplayBuffer(action_size, BUFFER_SIZE, BATCH_SIZE, random_seed)
def step(self, state, action, reward, next_state, done, agent_number):
"""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 and the learning interval
if len(self.memory) > BATCH_SIZE and self.timestep % LEARN_EVERY == 0:
for _ in range(LEARN_NUM):
experiences = self.memory.sample()
self.learn(experiences, GAMMA, agent_number)
def act(self, states, add_noise):
"""Returns actions for both agents as per current policy, given their respective states."""
states = torch.from_numpy(states).float().to(device)
actions = np.zeros((self.num_agents, self.action_size))
self.actor_local.eval()
with torch.no_grad():
# get action for each agent and concatenate them
for agent_num, state in enumerate(states):
action = self.actor_local(state).cpu().data.numpy()
actions[agent_num, :] = action
self.actor_local.train()
# add noise to actions
if add_noise:
actions += self.eps * self.noise.sample()
return np.clip(actions, -1, 1)
def reset(self):
self.noise.reset()
def learn(self, experiences, gamma, agent_number):
"""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)
if agent_number == 0:
actions_next = torch.cat((actions_next, actions[:,2:]), dim=1)
else:
actions_next = torch.cat((actions[:,:2], actions_next), dim=1)
# Compute Q targets for current states (y_i)
Q_targets_next = self.critic_target(next_states, actions_next)
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()
torch.nn.utils.clip_grad_norm_(self.critic_local.parameters(), 1)
self.critic_optimizer.step()
# ---------------------------- update actor ---------------------------- #
# Compute actor loss
actions_pred = self.actor_local(states)
if agent_number == 0:
actions_pred = torch.cat((actions_pred, actions[:,2:]), dim=1)
else:
actions_pred = torch.cat((actions[:,:2], actions_pred), dim=1)
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)
# ---------------------------- update noise ---------------------------- #
self.eps -= self.eps_decay
self.eps = max(self.eps, 0)
self.noise.reset()
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, seed, mu=0.0, theta=OU_THETA, sigma=OU_SIGMA):
"""Initialize parameters and noise process."""
self.mu = mu * np.ones(size)
self.theta = theta
self.sigma = sigma
self.seed = random.seed(seed)
self.size = size
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.random.standard_normal(self.size) # this change is fundamental to learning
self.state = x + dx
return self.state
class ReplayBuffer:
"""Fixed-size buffer to store experience tuples."""
def __init__(self, action_size, buffer_size, batch_size, seed):
"""Initialize a ReplayBuffer object.
Params
======
buffer_size (int): maximum size of buffer
batch_size (int): size of each training batch
"""
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"])
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])).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)