def __init__(self,
state_size,
action_size,
random_seed,
memory,
hyper_param=None):
"""Initialize an Agent object.
Params
======
state_size (int): dimension of each state
action_size (int): dimension of each action
random_seed (int): random seed
"""
if hyper_param is None:
hyper_param = HyperParam()
hyper_param.actor_fc1 = 128
hyper_param.actor_fc2 = 128
hyper_param.critic_fc1 = 128
hyper_param.critic_fc2 = 128
hyper_param.lr_actor = LR_ACTOR
hyper_param.lr_critic = LR_CRITIC
hyper_param.tau = TAU
self.hyper_param = hyper_param
self.state_size = state_size
self.action_size = action_size
self.seed = random.seed(random_seed)
# Actor Network (w/ Target Network)
self.actor_local = Actor(state_size, action_size, random_seed,
hyper_param.actor_fc1,
hyper_param.actor_fc2).to(device)
self.actor_target = Actor(state_size, action_size, random_seed,
hyper_param.actor_fc1,
hyper_param.actor_fc2).to(device)
self.actor_optimizer = optim.Adam(self.actor_local.parameters(),
lr=hyper_param.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)
# Initialize and local to target to be the same
# self.soft_update(self.critic_local, self.critic_target, tau=1.0)
# self.soft_update(self.actor_local, self.actor_target, tau=1.0)
# Noise process
self.noise = OUNoise(action_size, random_seed)
self.memory = memory # ReplayBuffer(action_size, BUFFER_SIZE, BATCH_SIZE, random_seed)
def __init__(self, state_size, action_size, random_seed):
# 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.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)
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)
self.noise = OUNoise(action_size, random_seed)
self.memory = ReplayBuffer(action_size, BUFFER_SIZE, BATCH_SIZE, random_seed)
class DDPGAgent():
# Interacts with and learns from the environment
def __init__(self, state_size, action_size, random_seed):
# 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.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)
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)
self.noise = OUNoise(action_size, random_seed)
self.memory = ReplayBuffer(action_size, BUFFER_SIZE, BATCH_SIZE, random_seed)
def step(self, time_step, state, action, reward, next_state, done):
# Save experience in replay memory, and use random sample from buffer to learn
self.memory.add(state, action, reward, next_state, done)
if time_step % N_TIME_STEPS != 0:
return
if len(self.memory) > BATCH_SIZE:
for i in range(N_LEARN_UPDATES):
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(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 DDPGAgent():
"""Interacts with and learns from the environment."""
memory = None
actor_local = None
actor_target = None
actor_optimizer = None
critic_local = None
critic_target = None
critic_optimizer = None
def __init__(self,
state_size,
action_size,
random_seed,
memory,
hyper_param=None):
"""Initialize an Agent object.
Params
======
state_size (int): dimension of each state
action_size (int): dimension of each action
random_seed (int): random seed
"""
if hyper_param is None:
hyper_param = HyperParam()
hyper_param.actor_fc1 = 128
hyper_param.actor_fc2 = 128
hyper_param.critic_fc1 = 128
hyper_param.critic_fc2 = 128
hyper_param.lr_actor = LR_ACTOR
hyper_param.lr_critic = LR_CRITIC
hyper_param.tau = TAU
self.hyper_param = hyper_param
self.state_size = state_size
self.action_size = action_size
self.seed = random.seed(random_seed)
# Actor Network (w/ Target Network)
self.actor_local = Actor(state_size, action_size, random_seed,
hyper_param.actor_fc1,
hyper_param.actor_fc2).to(device)
self.actor_target = Actor(state_size, action_size, random_seed,
hyper_param.actor_fc1,
hyper_param.actor_fc2).to(device)
self.actor_optimizer = optim.Adam(self.actor_local.parameters(),
lr=hyper_param.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)
# Initialize and local to target to be the same
# self.soft_update(self.critic_local, self.critic_target, tau=1.0)
# self.soft_update(self.actor_local, self.actor_target, tau=1.0)
# Noise process
self.noise = OUNoise(action_size, random_seed)
self.memory = memory # ReplayBuffer(action_size, BUFFER_SIZE, BATCH_SIZE, random_seed)
def step(self, time_step):
"""Save experience in replay memory, and use random sample from buffer to learn."""
# self.memory.add(state, action, reward, next_state, done)
if time_step % N_TIME_STEPS != 0:
return
if len(self.memory) > BATCH_SIZE:
for i in range(N_LEARN_UPDATES):
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(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_targets, Q_expected)
# 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)
def __init__(self,
state_size,
action_size,
num_agents,
random_seed,
hyper_param=None):
"""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.num_agents = num_agents
if hyper_param is None:
hyper_param = HyperParam()
hyper_param.epsilon = True
hyper_param.epsilon_decay = EXPLORE_EXPLOIT_DECAY
hyper_param.epsilon_spaced_init = 100
hyper_param.epsilon_spaced_decay = 1.5
hyper_param.actor_fc1 = 128
hyper_param.actor_fc2 = 128
hyper_param.critic_fc1 = 128
hyper_param.critic_fc2 = 128
hyper_param.lr_actor = 1e-3
hyper_param.lr_critic = 1e-3
hyper_param.tau = 1e-4
hyper_param.batch_size = 128
hyper_param.n_learn_updates = 10
hyper_param.n_time_steps = 20
self.hyper_param = hyper_param
self.device = device
self.memory = ReplayBuffer(action_size, BUFFER_SIZE,
hyper_param.batch_size, random_seed)
# Actor Network (w/ Target Network)
self.actor_local = Actor(state_size, action_size, random_seed,
hyper_param.actor_fc1,
hyper_param.actor_fc2).to(device)
self.actor_target = Actor(state_size, action_size, random_seed,
hyper_param.actor_fc1,
hyper_param.actor_fc2).to(device)
self.actor_optimizer = optim.Adam(self.actor_local.parameters(),
lr=hyper_param.lr_actor)
# Critic Network (w/ Target Network)
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=hyper_param.lr_critic,
weight_decay=WEIGHT_DECAY)
# self.epsilon = SpacedRepetitionDecay(ExponentialDecay(1.0, 0.0, hyper_param.epsilon_decay),
# hyper_param.epsilon_spaced_init, hyper_param.epsilon_spaced_decay)
self.epsilon = PositiveMemoriesFactorExplorationDecay(
0.5, 0, 0.0002, 0.12, self.memory)
self.noise = OUNoise(action_size, random_seed)
self.train_mode = True
self.actor_loss = []
self.critic_loss = []
class MADDPGAgent2():
"""Interacts with and learns from the environment."""
def __init__(self,
state_size,
action_size,
num_agents,
random_seed,
hyper_param=None):
"""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.num_agents = num_agents
if hyper_param is None:
hyper_param = HyperParam()
hyper_param.epsilon = True
hyper_param.epsilon_decay = EXPLORE_EXPLOIT_DECAY
hyper_param.epsilon_spaced_init = 100
hyper_param.epsilon_spaced_decay = 1.5
hyper_param.actor_fc1 = 128
hyper_param.actor_fc2 = 128
hyper_param.critic_fc1 = 128
hyper_param.critic_fc2 = 128
hyper_param.lr_actor = 1e-3
hyper_param.lr_critic = 1e-3
hyper_param.tau = 1e-4
hyper_param.batch_size = 128
hyper_param.n_learn_updates = 10
hyper_param.n_time_steps = 20
self.hyper_param = hyper_param
self.device = device
self.memory = ReplayBuffer(action_size, BUFFER_SIZE,
hyper_param.batch_size, random_seed)
# Actor Network (w/ Target Network)
self.actor_local = Actor(state_size, action_size, random_seed,
hyper_param.actor_fc1,
hyper_param.actor_fc2).to(device)
self.actor_target = Actor(state_size, action_size, random_seed,
hyper_param.actor_fc1,
hyper_param.actor_fc2).to(device)
self.actor_optimizer = optim.Adam(self.actor_local.parameters(),
lr=hyper_param.lr_actor)
# Critic Network (w/ Target Network)
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=hyper_param.lr_critic,
weight_decay=WEIGHT_DECAY)
# self.epsilon = SpacedRepetitionDecay(ExponentialDecay(1.0, 0.0, hyper_param.epsilon_decay),
# hyper_param.epsilon_spaced_init, hyper_param.epsilon_spaced_decay)
self.epsilon = PositiveMemoriesFactorExplorationDecay(
0.5, 0, 0.0002, 0.12, self.memory)
self.noise = OUNoise(action_size, random_seed)
self.train_mode = True
self.actor_loss = []
self.critic_loss = []
def train(self, mode=True):
self.train_mode = mode
def step(self, time_step, state, action, reward, next_state, done):
"""Save experience in replay memory, and use random sample from buffer to learn."""
self.memory.add(
np.reshape(state, (self.state_size * self.num_agents, )),
np.reshape(action, (self.action_size * self.num_agents, )),
np.reshape(reward, (self.num_agents, )),
np.reshape(next_state, (self.state_size * self.num_agents, )),
np.reshape(done, (self.num_agents, )))
# if time_step % self.hyper_param.n_time_steps != 0:
# return
if len(self.memory) > self.hyper_param.batch_size:
# for i in range(self.hyper_param.n_learn_updates):
experiences = self.memory.sample()
self.learn(experiences, GAMMA)
def _act(self, state, add_noise=True):
"""Returns actions for given state as per current policy."""
if self.hyper_param.epsilon and self.train_mode and random.random(
) < self.epsilon.next():
return 2 * np.random.random_sample(self.action_size) - 1
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 act(self, state, add_noise=True):
"""Returns actions for given state as per current policy."""
return [self._act(state[i], add_noise) for i in range(self.num_agents)]
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_sum, actions_sum, rewards_sum, next_states_sum, dones_sum = experiences
batch_size = len(states_sum)
states = np.reshape(states_sum, (self.num_agents, batch_size, -1))
actions = np.reshape(actions_sum, (self.num_agents, batch_size, -1))
rewards = np.reshape(rewards_sum, (self.num_agents, batch_size, -1))
next_states = np.reshape(next_states_sum,
(self.num_agents, batch_size, -1))
dones = np.reshape(dones_sum, (self.num_agents, batch_size, -1))
# ---------------------------- update critic ---------------------------- #
# Get predicted next-state actions and Q values from target models
actions_next = [
self.actor_target(next_states[i]) for i in range(self.num_agents)
]
actions_next = torch.cat(actions_next, dim=1)
actions_next_sum = actions_next.view(
(batch_size, self.action_size * self.num_agents))
Q_targets_next_sum = self.critic_target(next_states_sum,
actions_next_sum)
# Compute Q targets for current states (y_i)
Q_targets_sum = rewards_sum + (gamma * Q_targets_next_sum *
(1 - dones_sum))
# Compute critic loss
Q_expected_sum = self.critic_local(states_sum, actions_sum)
critic_loss = F.mse_loss(Q_targets_sum, Q_expected_sum)
self.critic_loss.append(critic_loss.cpu().data.numpy())
# 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[i]) for i in range(self.num_agents)
]
actions_pred = torch.cat(actions_pred, dim=1)
actions_pred_sum = actions_pred.view(
(batch_size, self.action_size * self.num_agents))
actor_loss = -self.critic_local(states_sum, actions_pred_sum).mean()
self.actor_loss.append(actor_loss.cpu().data.numpy())
# Minimize the loss
self.actor_optimizer.zero_grad()
actor_loss.backward()
# torch.nn.utils.clip_grad_norm_(self.actor_local.parameters(), 1)
self.actor_optimizer.step()
# ----------------------- update target networks ----------------------- #
self.soft_update(self.critic_local, self.critic_target,
self.hyper_param.tau)
self.soft_update(self.actor_local, self.actor_target,
self.hyper_param.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)
def __init__(self,
state_size,
action_size,
num_agents,
random_seed,
hyper_param=None):
"""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.num_agents = num_agents
if hyper_param is None:
hyper_param = HyperParam()
hyper_param.epsilon = False
hyper_param.actor_fc1 = 128
hyper_param.actor_fc2 = 128
hyper_param.critic_fc1 = 128
hyper_param.critic_fc2 = 128
hyper_param.lr_actor = 1e-3
hyper_param.lr_critic = 1e-3
hyper_param.eps_actor = 1e-7
hyper_param.eps_critic = 1e-7
hyper_param.tau = 1e-4
hyper_param.buffer_size = int(1e6)
hyper_param.batch_size = 128
hyper_param.n_learn_updates = 10
hyper_param.n_time_steps = 20
hyper_param.gamma = 0.99
self.hyper_param = hyper_param
self.device = device
self.memory = ReplayBuffer(action_size, self.hyper_param.buffer_size,
self.hyper_param.batch_size, random_seed)
# Actor Network (w/ Target Network)
self.actor_local = Actor(state_size, action_size, random_seed,
hyper_param.actor_fc1,
hyper_param.actor_fc2).to(device)
self.actor_target = Actor(state_size, action_size, random_seed,
hyper_param.actor_fc1,
hyper_param.actor_fc2).to(device)
self.actor_optimizer = optim.Adam(self.actor_local.parameters(),
lr=hyper_param.lr_actor,
eps=hyper_param.eps_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=hyper_param.lr_critic,
eps=hyper_param.eps_critic)
self.hard_update(self.actor_target, self.actor_local)
self.hard_update(self.critic_target, self.critic_local)
self.noise = OUNoise(action_size, random_seed, mu=0.0)
self.train_mode = True
self.actor_loss = []
self.critic_loss = []
self.orig_actions = [[0.0, 0.0], [0.0, 0.0]]
if hyper_param.epsilon:
self.epsilon = hyper_param.epsilon_model(self.memory)
class MADDPGAgent3():
"""Interacts with and learns from the environment."""
def __init__(self,
state_size,
action_size,
num_agents,
random_seed,
hyper_param=None):
"""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.num_agents = num_agents
if hyper_param is None:
hyper_param = HyperParam()
hyper_param.epsilon = False
hyper_param.actor_fc1 = 128
hyper_param.actor_fc2 = 128
hyper_param.critic_fc1 = 128
hyper_param.critic_fc2 = 128
hyper_param.lr_actor = 1e-3
hyper_param.lr_critic = 1e-3
hyper_param.eps_actor = 1e-7
hyper_param.eps_critic = 1e-7
hyper_param.tau = 1e-4
hyper_param.buffer_size = int(1e6)
hyper_param.batch_size = 128
hyper_param.n_learn_updates = 10
hyper_param.n_time_steps = 20
hyper_param.gamma = 0.99
self.hyper_param = hyper_param
self.device = device
self.memory = ReplayBuffer(action_size, self.hyper_param.buffer_size,
self.hyper_param.batch_size, random_seed)
# Actor Network (w/ Target Network)
self.actor_local = Actor(state_size, action_size, random_seed,
hyper_param.actor_fc1,
hyper_param.actor_fc2).to(device)
self.actor_target = Actor(state_size, action_size, random_seed,
hyper_param.actor_fc1,
hyper_param.actor_fc2).to(device)
self.actor_optimizer = optim.Adam(self.actor_local.parameters(),
lr=hyper_param.lr_actor,
eps=hyper_param.eps_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=hyper_param.lr_critic,
eps=hyper_param.eps_critic)
self.hard_update(self.actor_target, self.actor_local)
self.hard_update(self.critic_target, self.critic_local)
self.noise = OUNoise(action_size, random_seed, mu=0.0)
self.train_mode = True
self.actor_loss = []
self.critic_loss = []
self.orig_actions = [[0.0, 0.0], [0.0, 0.0]]
if hyper_param.epsilon:
self.epsilon = hyper_param.epsilon_model(self.memory)
def train(self, mode=True):
self.train_mode = mode
def step(self, time_step, state, action, reward, next_state, done):
"""Save experience in replay memory, and use random sample from buffer to learn."""
if self.train_mode is False:
return
for i in range(self.num_agents):
self.memory.add(state[i], action[i], reward[i], next_state[i],
done[i])
if time_step % self.hyper_param.n_time_steps != 0:
return
if len(self.memory) > self.hyper_param.batch_size:
for i in range(self.hyper_param.n_learn_updates):
experiences = self.memory.sample()
self.learn(experiences, self.hyper_param.gamma)
def _act(self, state, add_noise=True):
"""Returns actions for given state as per current policy."""
batch_state = np.reshape(state, (1, self.state_size))
state = torch.from_numpy(batch_state).float().to(device)
self.actor_local.eval()
with torch.no_grad():
action = self.actor_local(state).cpu().data.numpy()
self.actor_local.train()
action = np.reshape(action, (self.action_size, ))
self.orig_actions = copy.copy(action)
if add_noise and self.train_mode:
eps = self.epsilon.next()
action = (1.0 - eps) * action + eps * self.noise.sample()
if self.hyper_param.epsilon:
random_action = 2 * np.random.random_sample(
self.action_size) - 1
action = (1.0 - eps) * action + eps * random_action
return np.clip(action, -1, 1)
def act(self, state, add_noise=True):
"""Returns actions for given state as per current policy."""
return [self._act(state[i], add_noise) for i in range(self.num_agents)]
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.smooth_l1_loss(Q_targets, Q_expected)
# critic_loss = F.mse_loss(Q_targets, Q_expected)
self.critic_loss.append(critic_loss.cpu().data.numpy())
# 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()
self.actor_loss.append(actor_loss.cpu().data.numpy())
# Minimize the loss
self.actor_optimizer.zero_grad()
actor_loss.backward()
# torch.nn.utils.clip_grad_norm_(self.actor_local.parameters(), 0.5)
self.actor_optimizer.step()
# ----------------------- update target networks ----------------------- #
self.soft_update(self.critic_local, self.critic_target,
self.hyper_param.tau)
self.soft_update(self.actor_local, self.actor_target,
self.hyper_param.tau)
def soft_update(self, from_model, to_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 to_param, from_param in zip(to_model.parameters(),
from_model.parameters()):
to_param.data.copy_(tau * from_param.data +
(1.0 - tau) * to_param.data)
def hard_update(self, target, source):
for target_param, param in zip(target.parameters(),
source.parameters()):
target_param.data.copy_(param.data)