class Agent():
"""Interacts with and learns from the environment."""
def __init__(self, state_size, action_size, random_seed, device="cpu"):
"""Initialize an Agent object.
Params
------
state_size : int
dimension of each state
action_size : int
dimension of each action
random_seed : int
random seed
"""
self.state_size = state_size
self.action_size = action_size
self.seed = random.seed(random_seed)
self.device = device
# Actor Network (w/ Target Network)
self.actor_local = Actor(state_size, action_size,
random_seed).to(self.device)
self.actor_local.apply(initialize_weights)
self.actor_target = Actor(state_size, action_size,
random_seed).to(self.device)
self.actor_target.apply(initialize_weights)
self.actor_target.eval()
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(self.device)
self.critic_local.apply(initialize_weights)
self.critic_target = Critic(state_size, action_size,
random_seed).to(self.device)
self.critic_target.apply(initialize_weights)
self.critic_target.eval()
self.critic_optimizer = optim.Adam(self.critic_local.parameters(),
lr=LR_CRITIC,
weight_decay=WEIGHT_DECAY)
# Noise process
self.noise = OUNoise(action_size,
random_seed + 1,
mu=0.,
theta=THETA,
sigma=SIGMA)
# Replay memory
self.memory = ReplayBuffer(action_size, BUFFER_SIZE, BATCH_SIZE,
random_seed + 2, self.device)
# 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, and use random sample from buffer to learn."""
# Save experience / reward
self.memory.add(state, action, reward, next_state, done)
self.t_step = (self.t_step + 1) % UPDATE_EVERY
# Learn, if enough samples are available in memory
if self.t_step == 0 and len(self.memory) > BATCH_SIZE:
for _ in range(N_LEARNING):
experiences = self.memory.sample()
self.learn(experiences, GAMMA)
def act(self, state, add_noise=True):
"""Returns actions for given state as per current policy."""
state = torch.from_numpy(state).float().to(self.device)
self.actor_local.eval()
with torch.no_grad():
action = self.actor_local(state).cpu().data.numpy()
self.actor_local.train()
if add_noise:
action += self.noise.sample()
return np.clip(action, -1, 1)
def reset(self):
self.noise.reset()
def learn(self, experiences, gamma):
"""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()
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)
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 Agent():
def __init__(self, n_state, n_action, n_agents, random_seed, device="cpu"):
"""Initialize an Agent object.
Params
------
n_state : int
dimension of each state
n_action : int
dimension of each action
random_seed : int
random seed
device :
which device is used, cpu or cuda.
"""
self.n_state = n_state
self.n_action = n_action
self.n_agents = n_agents
self.random_seed = np.random.seed(random_seed)
self.device = device
# Networks for the first agent
# Local Actor, Local Critic, Target Actor, Target Critic
self.actor_local1 = Actor(self.n_state, self.n_action,
self.random_seed).to(self.device)
self.actor_local1.apply(initialize_weights)
self.critic_local1 = Critic(self.n_state * self.n_agents,
self.n_action * self.n_agents,
self.random_seed).to(self.device)
self.critic_local1.apply(initialize_weights)
self.actor_target1 = Actor(self.n_state, self.n_action,
self.random_seed).to(self.device)
self.actor_target1.apply(initialize_weights)
self.actor_target1.eval()
self.critic_target1 = Critic(self.n_state * self.n_agents,
self.n_action * self.n_agents,
self.random_seed).to(self.device)
self.critic_target1.apply(initialize_weights)
self.critic_target1.eval()
# Networks for the second agent
# Local Actor, Local Critic, Target Actor, Target Critic
self.actor_local2 = Actor(self.n_state, self.n_action,
self.random_seed).to(self.device)
self.actor_local2.apply(initialize_weights)
self.critic_local2 = Critic(self.n_state * self.n_agents,
self.n_action * self.n_agents,
self.random_seed).to(self.device)
self.critic_local2.apply(initialize_weights)
self.actor_target2 = Actor(self.n_state, self.n_action,
self.random_seed).to(self.device)
self.actor_target2.apply(initialize_weights)
self.actor_target2.eval()
self.critic_target2 = Critic(self.n_state * self.n_agents,
self.n_action * self.n_agents,
self.random_seed).to(self.device)
self.actor_target2.apply(initialize_weights)
self.critic_target2.eval()
# optimizers
self.actor_optimizer1 = optim.Adam(self.actor_local1.parameters(),
lr=LR_ACTOR)
self.actor_optimizer2 = optim.Adam(self.actor_local2.parameters(),
lr=LR_ACTOR)
self.critic_optimizer1 = optim.Adam(self.critic_local1.parameters(),
lr=LR_CRITIC,
weight_decay=WEIGHT_DECAY)
self.critic_optimizer2 = optim.Adam(self.critic_local2.parameters(),
lr=LR_CRITIC,
weight_decay=WEIGHT_DECAY)
# Noise process
self.noise = OUNoise(n_action * 2,
random_seed + 1,
mu=0.,
theta=THETA,
sigma=SIGMA)
# Replay Buffer
self.memory = ReplayBuffer(n_action, BUFFER_SIZE, BATCH_SIZE,
random_seed + 2, self.device)
# Initialize time step (for updating every UPDATE_EVERY steps)
self.t_step = 0
def step(self, state, action, reward, next_state, done):
pass
# Save experience / reward
self.memory.add(state, action, reward, next_state, done)
self.t_step = (self.t_step + 1) % UPDATE_EVERY
# Learn, if enough samples are available in memory
if self.t_step == 0 and len(self.memory) > BATCH_SIZE:
for _ in range(N_LEARNING):
experiences = self.memory.sample()
self.learn(experiences, GAMMA)
def act(self, state, add_noise=True):
state0 = torch.from_numpy(state[0]).unsqueeze(dim=0).float().to(
self.device)
state1 = torch.from_numpy(state[1]).unsqueeze(dim=0).float().to(
self.device)
self.actor_local1.eval()
self.actor_local2.eval()
with torch.no_grad():
action0 = self.actor_local1(state0).cpu().data.numpy()
action1 = self.actor_local2(state1).cpu().data.numpy()
action = np.vstack([action0, action1])
self.actor_local1.train()
self.actor_local2.train()
if add_noise:
action += self.noise.sample()
return np.clip(action, -1, 1)
def reset(self):
self.noise.reset()
def learn(self, experiences, gamma):
states, actions, rewards, next_states, dones = experiences
# ---------------------------- update critic ---------------------------- #
# Get predicted next-state actions and Q values from target models
with torch.no_grad():
actions_next1 = self.actor_target1(next_states[:, 0:24])
actions_next2 = self.actor_target2(next_states[:, 24:])
actions_next = torch.cat((actions_next1, actions_next2), dim=1)
Q_targets_next1 = self.critic_target1(next_states, actions_next)
Q_targets_next2 = self.critic_target2(next_states, actions_next)
# Compute Q targets for current states (y_i)
Q_targets1 = rewards[:, 0].unsqueeze(
dim=1) + (gamma * Q_targets_next1 *
(1 - dones[:, 0].unsqueeze(dim=1)))
Q_targets2 = rewards[:, 1].unsqueeze(
dim=1) + (gamma * Q_targets_next2 *
(1 - dones[:, 1].unsqueeze(dim=1)))
# Compute critic loss
Q_expected1 = self.critic_local1(states, actions)
Q_expected2 = self.critic_local2(states, actions)
critic_loss1 = F.mse_loss(Q_expected1, Q_targets1.detach())
critic_loss2 = F.mse_loss(Q_expected2, Q_targets2.detach())
# Minimize the loss
self.critic_optimizer1.zero_grad()
critic_loss1.backward()
torch.nn.utils.clip_grad_norm_(self.critic_local1.parameters(), 1)
self.critic_optimizer1.step()
self.critic_optimizer2.zero_grad()
critic_loss2.backward()
torch.nn.utils.clip_grad_norm_(self.critic_local2.parameters(), 1)
self.critic_optimizer2.step()
# ---------------------------- update actor ---------------------------- #
# Compute actor loss
actions_pred1 = self.actor_local1(states[:, 0:24])
actions_pred2 = self.actor_local2(states[:, 24:])
actions_pred = torch.cat((actions_pred1, actions_pred2), dim=1)
actor_loss1 = -self.critic_local1(states, actions_pred).mean()
# Minimize the loss
self.actor_optimizer1.zero_grad()
actor_loss1.backward(retain_graph=True)
self.actor_optimizer1.step()
actor_loss2 = -self.critic_local2(states, actions_pred).mean()
self.actor_optimizer2.zero_grad()
actor_loss2.backward(retain_graph=True)
self.actor_optimizer2.step()
# ----------------------- update target networks ----------------------- #
self.soft_update(self.critic_local1, self.critic_target1, TAU)
self.soft_update(self.actor_local1, self.actor_target1, TAU)
self.soft_update(self.critic_local2, self.critic_target2, TAU)
self.soft_update(self.actor_local2, self.actor_target2, TAU)
def soft_update(self, local_model, target_model, tau):
"""Soft update model parameters
Arguments
"""
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)