def __init__(self,
state_size,
action_size,
num_agents,
random_seed,
lr_actor=1e-4,
lr_critic=1e-3,
fc1_units=400,
fc2_units=300,
buffer_size=int(1e5),
batch_size=128,
gamma=0.99,
tau=1e-3,
max_norm=1.0,
learn_period=20,
learn_sampling_num=10):
"""Initialize an Agent object.
Args:
state_size (int): dimension of each state
action_size (int): dimension of each action
random_seed (int): random seed
max_norm (float): value of clip_grad_norm for critic optimizer
"""
super().__init__()
self.state_size = state_size
self.num_agents = num_agents
self.action_size = action_size
self.seed = random.seed(random_seed)
self.max_norm = max_norm
self.learn_period = learn_period
self.learn_sampling_num = learn_sampling_num
# Actor Network (w/ Target Network)
self.actor_local = DDPGActor(state_size,
action_size,
random_seed,
fc1_units=fc1_units,
fc2_units=fc2_units).to(device)
self.actor_target = DDPGActor(state_size,
action_size,
random_seed,
fc1_units=fc1_units,
fc2_units=fc2_units).to(device)
self.actor_optimizer = optim.Adam(self.actor_local.parameters(),
lr=lr_actor)
# Critic Network (w/ Target Network)
self.critic_local = DDPGCritic(state_size,
action_size,
random_seed,
fcs1_units=fc1_units,
fc2_units=fc2_units).to(device)
self.critic_target = DDPGCritic(state_size,
action_size,
random_seed,
fcs1_units=fc1_units,
fc2_units=fc2_units).to(device)
self.critic_optimizer = optim.Adam(self.critic_local.parameters(),
lr=lr_critic)
# Noise process for action
# Noise process
self.exploration_mu = 0
self.exploration_theta = 0.15 # (Timothy Lillicrap, 2016)
self.exploration_sigma = 0.2 # (Timothy Lillicrap, 2016)
# self.noise = OUNoise(action_size, self.exploration_mu, self.exploration_theta, self.exploration_sigma)
self.noise = OUNoiseMultivariate((num_agents, action_size),
random_seed,
mu=self.exploration_mu,
theta=self.exploration_theta,
sigma=self.exploration_sigma)
# Replay memory
self.memory = ReplayBuffer(action_size, buffer_size, batch_size,
random_seed, device)
# parameter of discounted reward
self.gamma = gamma
# soft update parameter
self.tau = tau
self.batch_size = batch_size
class DDPGAgentVersion3(BaseAgent):
def __init__(self,
state_size,
action_size,
num_agents,
random_seed,
lr_actor=1e-4,
lr_critic=1e-3,
fc1_units=400,
fc2_units=300,
buffer_size=int(1e5),
batch_size=128,
gamma=0.99,
tau=1e-3,
max_norm=1.0,
learn_period=20,
learn_sampling_num=10):
"""Initialize an Agent object.
Args:
state_size (int): dimension of each state
action_size (int): dimension of each action
random_seed (int): random seed
max_norm (float): value of clip_grad_norm for critic optimizer
"""
super().__init__()
self.state_size = state_size
self.num_agents = num_agents
self.action_size = action_size
self.seed = random.seed(random_seed)
self.max_norm = max_norm
self.learn_period = learn_period
self.learn_sampling_num = learn_sampling_num
# Actor Network (w/ Target Network)
self.actor_local = DDPGActor(state_size,
action_size,
random_seed,
fc1_units=fc1_units,
fc2_units=fc2_units).to(device)
self.actor_target = DDPGActor(state_size,
action_size,
random_seed,
fc1_units=fc1_units,
fc2_units=fc2_units).to(device)
self.actor_optimizer = optim.Adam(self.actor_local.parameters(),
lr=lr_actor)
# Critic Network (w/ Target Network)
self.critic_local = DDPGCritic(state_size,
action_size,
random_seed,
fcs1_units=fc1_units,
fc2_units=fc2_units).to(device)
self.critic_target = DDPGCritic(state_size,
action_size,
random_seed,
fcs1_units=fc1_units,
fc2_units=fc2_units).to(device)
self.critic_optimizer = optim.Adam(self.critic_local.parameters(),
lr=lr_critic)
# Noise process for action
# Noise process
self.exploration_mu = 0
self.exploration_theta = 0.15 # (Timothy Lillicrap, 2016)
self.exploration_sigma = 0.2 # (Timothy Lillicrap, 2016)
# self.noise = OUNoise(action_size, self.exploration_mu, self.exploration_theta, self.exploration_sigma)
self.noise = OUNoiseMultivariate((num_agents, action_size),
random_seed,
mu=self.exploration_mu,
theta=self.exploration_theta,
sigma=self.exploration_sigma)
# Replay memory
self.memory = ReplayBuffer(action_size, buffer_size, batch_size,
random_seed, device)
# parameter of discounted reward
self.gamma = gamma
# soft update parameter
self.tau = tau
self.batch_size = batch_size
def step(self, states, actions, rewards, next_states, dones, time_step):
"""Save experience in replay memory, and use random sample from buffer to learn."""
# Save experience / reward
for i in range(self.num_agents):
self.memory.add(states[i, :], actions[i, :], rewards[i],
next_states[i, :], dones[i])
#self.memory.add_batch(states, actions, rewards, next_states, dones)
# Learn, if enough samples are available in memory
if (len(self.memory) > self.batch_size) and (time_step %
self.learn_period == 0):
for _ in range(self.learn_sampling_num):
experiences = self.memory.sample()
self.learn(experiences, self.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 + gamma * critic_target(next_state, actor_target(next_state))
where:
actor_target(state) -> action
critic_target(state, action) -> Q-value
Args:
experiences (Tuple[torch.Tensor]): tuple of (s, a, r, s', done) tuples
gamma (float): discount factor
"""
states, actions, rewards, next_states, dones = experiences
# train critic
# loss fuction = Q_target(TD 1-step boostrapping) - Q_local(current)
actions_next = self.actor_target(next_states)
Q_targets_next = self.critic_target(next_states, actions_next)
Q_targets = rewards + (gamma * Q_targets_next * (1 - dones))
Q_expected = self.critic_local(states, actions)
critic_loss = F.mse_loss(Q_expected, Q_targets)
self.critic_optimizer.zero_grad()
critic_loss.backward()
torch.nn.utils.clip_grad_norm_(self.critic_local.parameters(),
self.max_norm)
self.critic_optimizer.step()
# train actor (policy gradient)
actions_pred = self.actor_local(states)
actor_loss = -self.critic_local(states, actions_pred).mean()
self.actor_optimizer.zero_grad()
actor_loss.backward()
self.actor_optimizer.step()
# update critic_target
self.soft_update(self.critic_local, self.critic_target, self.tau)
# update actor_target
self.soft_update(self.actor_local, self.actor_target, self.tau)
def soft_update(self, local_model, target_model, tau):
"""Soft update model parameters.
θ_target = τ*θ_local + (1 - τ)*θ_target
Args:
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 model_dicts(self):
return {'actor': self.actor_target, 'critic': self.critic_target}