class Agent:
def __init__(self, replay_buffer, noise, state_dim, action_dim, seed, fc1_units = 256, fc2_units = 128,
device="cpu", lr_actor=1e-4, lr_critic=1e-3, batch_size=128, discount=0.99, tau=1e-3):
torch.manual_seed(seed)
self.actor_local = Actor(state_dim, action_dim, fc1_units, fc2_units, seed).to(device)
self.critic_local = Critic(state_dim, action_dim, fc1_units, fc2_units, seed).to(device)
self.actor_optimizer = optim.Adam(params=self.actor_local.parameters(), lr=lr_actor)
self.critic_optimizer = optim.Adam(params=self.critic_local.parameters(), lr=lr_critic)
self.actor_target = Actor(state_dim, action_dim, fc1_units, fc2_units, seed).to(device)
self.critic_target = Critic(state_dim, action_dim, fc1_units, fc2_units, seed).to(device)
self.buffer = replay_buffer
self.noise = noise
self.device = device
self.batch_size = batch_size
self.discount = discount
self.tau = tau
Agent.hard_update(model_local=self.actor_local, model_target=self.actor_target)
Agent.hard_update(model_local=self.critic_local, model_target=self.critic_target)
def step(self, states, actions, rewards, next_states, dones):
for state, action, reward, next_state, done in zip(states, actions, rewards, next_states, dones):
self.buffer.add(state=state, action=action, reward=reward, next_state=next_state, done=done)
if self.buffer.size() >= self.batch_size:
experiences = self.buffer.sample(self.batch_size)
self.learn(self.to_tensor(experiences))
def to_tensor(self, experiences):
states, actions, rewards, next_states, dones = experiences
states = torch.from_numpy(states).float().to(self.device)
actions = torch.from_numpy(actions).float().to(self.device)
rewards = torch.from_numpy(rewards).float().to(self.device)
next_states = torch.from_numpy(next_states).float().to(self.device)
dones = torch.from_numpy(dones.astype(np.uint8)).float().to(self.device)
return states, actions, rewards, next_states, dones
def act(self, states, add_noise=True):
states = torch.from_numpy(states).float().to(device=self.device)
self.actor_local.eval()
with torch.no_grad():
actions = self.actor_local(states).data.numpy()
self.actor_local.train()
if add_noise:
actions += self.noise.sample()
return np.clip(actions, -1, 1)
def learn(self, experiences):
states, actions, rewards, next_states, dones = experiences
# Update critic
next_actions = self.actor_target(next_states)
q_target_next = self.critic_target(next_states, next_actions)
q_target = rewards + self.discount * q_target_next * (1.0 - dones)
q_local = self.critic_local(states, actions)
critic_loss = F.mse_loss(input=q_local, target=q_target)
self.critic_local.zero_grad()
critic_loss.backward()
self.critic_optimizer.step()
actor_objective = self.critic_local(states, self.actor_local(states)).mean()
self.actor_local.zero_grad()
(-actor_objective).backward()
self.actor_optimizer.step()
Agent.soft_update(model_local=self.critic_local, model_target=self.critic_target, tau=self.tau)
Agent.soft_update(model_local=self.actor_local, model_target=self.actor_target, tau=self.tau)
@staticmethod
def soft_update(model_local, model_target, tau):
for local_param, target_param in zip(model_local.parameters(), model_target.parameters()):
target_param.data.copy_(tau * local_param.data + (1.0 - tau) * target_param.data)
@staticmethod
def hard_update(model_local, model_target):
Agent.soft_update(model_local=model_local, model_target=model_target, tau=1.0)
def reset(self):
self.noise.reset()
class Agent():
def __init__(self,
state_size, action_size, replay_memory, random_seed=0, nb_agent = 20, bs = 128,
gamma=0.99, tau=1e-3, lr_actor=1e-4, lr_critic=1e-4, wd_actor=0, wd_critic=0,
clip_actor = None, clip_critic=None, update_interval = 20, update_times = 10):
self.state_size = state_size
self.action_size = action_size
self.seed = random.seed(random_seed)
self.nb_agent = nb_agent
self.bs = bs
self.update_interval = update_interval
self.update_times = update_times
self.timestep = 0
self.gamma = gamma
self.tau = tau
self.lr_actor = lr_actor
self.lr_critic = lr_critic
self.wd_critic = wd_critic
self.wd_actor = wd_actor
self.clip_critic=clip_critic
self.clip_actor = clip_actor
self.actor_losses = []
self.critic_losses = []
# Actor #0
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=self.lr_actor,weight_decay=self.wd_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=self.lr_critic,weight_decay=self.wd_critic)
# Noise process
self.noise = OUNoise((self.nb_agent, action_size), random_seed)
# Replay memory
self.memory = replay_memory
def step(self, states, actions, rewards, next_states, dones):
"""Save experience in replay memory, and use random sample from buffer to learn."""
#increment timestep
self.timestep+=1
# Save experience / reward
for state, action, reward, next_state, done in zip(states, actions, rewards, next_states, dones):
self.memory.add(state, action, reward, next_state, done)
# Learn, if enough samples are available in memory
if self.timestep % self.update_interval == 0:
for i in range(self.update_times):
if len(self.memory) > self.bs:
experiences = self.memory.sample(self.bs)
self.learn(experiences)
def act(self, state, add_noise=True):
"""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_noise:
action += self.noise.sample()
return np.clip(action, -1, 1)
def reset_noise(self):
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
"""
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 + (self.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()
if self.clip_critic: torch.nn.utils.clip_grad_norm_(self.critic_local.parameters(), self.clip_critic)
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()
if self.clip_actor: torch.nn.utils.clip_grad_norm_(self.actor_local.parameters(), self.clip_actor)
self.actor_optimizer.step()
# ----------------------- update target networks ----------------------- #
self.soft_update(self.critic_local, self.critic_target)
self.soft_update(self.actor_local, self.actor_target)
self.actor_losses.append(actor_loss.cpu().data.numpy())
self.critic_losses.append(critic_loss.cpu().data.numpy())
def soft_update(self, local_model, target_model):
"""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_(self.tau*local_param.data + (1.0-self.tau)*target_param.data)
class Agent():
"""Interacts with and learns from the environment."""
def __init__(self, state_size, action_size, num_agents, random_seed):
"""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.num_agents = num_agents
self.seed = random.seed(random_seed)
self.eps = eps_start
self.eps_decay = 1 / (eps_p * LEARN_NUM
) # set decay rate based on epsilon end target
# 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,
timestep):
"""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 at learning interval settings
if len(self.memory) > BATCH_SIZE and timestep % 1 == 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()
actions = np.clip(actions, -1, 1)
return actions
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)
# Construct next actions vector relative to the agent
if agent_number != 0:
actions_next = torch.cat((actions[:, :2], actions_next), dim=1)
else:
actions_next = torch.cat((actions_next, actions[:, 2:]), 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)
# Construct action prediction vector relative to each agent
if agent_number != 0:
actions_pred = torch.cat((actions[:, :2], actions_pred), dim=1)
else:
actions_pred = torch.cat((actions_pred, actions[:, 2:]), dim=1)
# Compute actor loss
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 decay parameter
self.eps -= self.eps_decay
self.eps = max(self.eps, eps_end)
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)
