class Agent():
def __init__(self, alpha = 0.0003, beta = 0.0003, input_dims = [8],
env = None, gamma = 0.99, tau = 0.005, n_actions = 2, max_size = 1000000,
layer1_size = 256, layer2_size = 256, batch_size = 256, reward_scale = 2):
self.gamma = gamma
self.tau = tau
self.batch_size = batch_size
self.n_actions = n_actions
self.scale = reward_scale
self.memory = ReplayBuffer(max_size, input_dims, n_actions = n_actions)
self.actor = ActorNetwork(alpha, input_dims, n_actions = n_actions, max_action = env.action_space.high)
self.critic1 = CriticNetwork(beta, input_dims, n_actions = n_actions, name = 'critic1')
self.critic2 = CriticNetwork(beta, input_dims, n_actions = n_actions, name = 'critic2')
self.value = ValueNetwork(beta, input_dims, name = 'value')
self.target_value = ValueNetwork(beta, input_dims, name = 'target')
self.device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
self.update_network_params(tau = 1)
def choose_action(self, obs):
state = torch.tensor([obs],dtype=torch.float32).to(self.device)
actions, _ = self.actor.sample_normal(state, reparam = False)
return actions.cpu().detach().numpy()[0]
def store_trans(self, state, action, reward, new_state, done):
self.memory.store_trans(state, action, reward, new_state, done)
def update_network_params(self, tau = None):
if tau is None:
tau = self.tau
target_value_params = self.target_value.named_parameters()
value_params = self.value.named_parameters()
target_value_state_dict = dict(target_value_params)
value_state_dict = dict(value_params)
for name in value_state_dict.keys():
value_state_dict[name] = tau * value_state_dict[name].clone() + \
(1 - tau) * target_value_state_dict[name].clone()
self.target_value.load_state_dict(value_state_dict)
def save_models(self):
self.actor.save_checkpoint()
self.value.save_checkpoint()
self.target_value.save_checkpoint()
self.critic1.save_checkpoint()
self.critic2.save_checkpoint()
print('saving models')
def load_models(self):
self.actor.load_checkpoint()
self.value.load_checkpoint()
self.target_value.load_checkpoint()
self.critic1.load_checkpoint()
self.critic2.load_checkpoint()
print('loading models')
def get_critic_val_log_prob(self, state, reparam):
actions, log_probs = self.actor.sample_normal(state, reparam = False)
log_probs = log_probs.view(-1)
q1_new = self.critic1(state, actions)
q2_new = self.critic2(state, actions)
critic_value = torch.min(q1_new, q2_new)
critic_value = critic_value.view(-1)
return log_probs, critic_value
def learn(self):
if self.memory.mem_counter < self.batch_size:
return
state, action, reward, new_state, done = \
self.memory.sample_buffer(self.batch_size)
reward = torch.tensor(reward, dtype=torch.float).to(self.actor.device)
done = torch.tensor(done).to(self.actor.device)
state_ = torch.tensor(new_state, dtype=torch.float).to(self.actor.device)
state = torch.tensor(state, dtype=torch.float).to(self.actor.device)
action = torch.tensor(action, dtype=torch.float).to(self.actor.device)
value = self.value(state).view(-1)
value_ = self.target_value(state_).view(-1)
value_[done] = 0.0
actions, log_probs = self.actor.sample_normal(state, reparam=False)
log_probs = log_probs.view(-1)
q1_new_policy = self.critic1.forward(state, actions)
q2_new_policy = self.critic2.forward(state, actions)
critic_value = torch.min(q1_new_policy, q2_new_policy)
critic_value = critic_value.view(-1)
self.value.optimizer.zero_grad()
value_target = critic_value - log_probs
value_loss = 0.5 * F.mse_loss(value, value_target)
value_loss.backward(retain_graph=True)
self.value.optimizer.step()
actions, log_probs = self.actor.sample_normal(state, reparam=True)
log_probs = log_probs.view(-1)
q1_new_policy = self.critic1.forward(state, actions)
q2_new_policy = self.critic2.forward(state, actions)
critic_value = torch.min(q1_new_policy, q2_new_policy)
critic_value = critic_value.view(-1)
actor_loss = log_probs - critic_value
actor_loss = torch.mean(actor_loss)
self.actor.optimizer.zero_grad()
actor_loss.backward(retain_graph=True)
self.actor.optimizer.step()
self.critic1.optimizer.zero_grad()
self.critic2.optimizer.zero_grad()
q_hat = self.scale*reward + self.gamma*value_
q1_old_policy = self.critic1.forward(state, action).view(-1)
q2_old_policy = self.critic2.forward(state, action).view(-1)
critic1_loss = 0.5 * F.mse_loss(q1_old_policy, q_hat)
critic2_loss = 0.5 * F.mse_loss(q2_old_policy, q_hat)
critic_loss = critic1_loss + critic2_loss
critic_loss.backward()
self.critic1.optimizer.step()
self.critic2.optimizer.step()
self.update_network_params()
class Agent():
"""Interacts with and learns from the environment."""
def __init__(self,
state_size,
action_size,
lr_actor=LR_ACTOR,
lr_critic=LR_CRITIC,
random_seed=42,
num_agents=1):
"""Initialize Agent object.
Params
====
state_size (int): Dimension of each state
action_size (int): Dimension of each action
lr_actor (float): Learning rate for actor model
lr_critic (float): Learning Rate for critic model
random_seed (int): Random seed
num_agents (int): Number of agents
return
====
None
"""
self.state_size = state_size
self.action_size = action_size
self.seed = random.seed(random_seed)
self.num_agents = num_agents
# Initialize time step (for updating every hyperparameters["update_every"] steps)
self.t_step = 0
# Actor network
self.actor = ActorNetwork(lr_actor,
state_size,
action_size,
random_seed,
name="actor")
self.actor_target = ActorNetwork(lr_actor,
state_size,
action_size,
random_seed,
name="actor_target")
self.soft_update(self.actor, self.actor_target, tau=1)
# Critic network
self.critic = CriticNetwork(lr_critic,
state_size,
action_size,
random_seed,
name="critic")
self.critic_target = CriticNetwork(lr_critic,
state_size,
action_size,
random_seed,
name="critic_target")
self.soft_update(self.critic, self.critic_target, tau=1)
# Noise process
self.noise = OUActionNoise(mu=np.zeros(action_size))
# Replay buffer memory
self.memory = ReplayBuffer(action_size, BUFFER_SIZE, BATCH_SIZE,
random_seed)
def step(self, states, actions, rewards, next_states, dones):
"""Save experience in replay memory, and use random sample from buffer to learn."""
# Save experience / reward
# Support for multi agents learners
for state, action, reward, next_state, done in zip(
states, actions, rewards, next_states, dones):
self.memory.add(state, action, reward, next_state, done)
# Update timestep to learn
self.t_step = (self.t_step + 1) % UPDATE_EVERY
# Learn, if enough samples are available in memory
if len(self.memory) > BATCH_SIZE and self.t_step == 0:
experiences = self.memory.sample()
self.learn(experiences, GAMMA)
def act(self, state, add_noise=True):
"""Returns actions for given state as per current policy."""
states = T.from_numpy(state).float().to(device)
self.actor.eval()
with T.no_grad():
actions = self.actor(states).cpu().data.numpy()
self.actor.train()
if add_noise:
actions += self.noise.sample()
return np.clip(actions, -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(states, actions)
critic_loss = F.mse_loss(Q_expected, Q_targets)
# Minimize the loss
self.critic.optimizer.zero_grad()
critic_loss.backward()
T.nn.utils.clip_grad_norm_(self.critic.parameters(), 1.0)
self.critic.optimizer.step()
# ---------------------------- update actor ---------------------------- #
# Compute actor loss
actions_pred = self.actor(states)
actor_loss = -self.critic(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, self.critic_target, TAU)
self.soft_update(self.actor, 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 save_models(self):
""" Save models weights """
self.actor.save_checkpoint()
self.critic.save_checkpoint()
self.actor_target.save_checkpoint()
self.critic_target.save_checkpoint()
def load_models(self):
""" Load models weights """
self.actor.load_checkpoint()
self.critic.load_checkpoint()
self.actor_target.load_checkpoint()
self.critic_target.load_checkpoint()
class Agent:
def __init__(self, input_size, output_size, hidden = 256, lr_actor=1.0e-3, lr_critic=1.0e-3, agent_number=0, tau=1.0e-2,
gamma=0.99, epsilon=1.0, epsilon_decay=0.99, weight_decay=0, clipgrad=.1, seed = 42):
super(Agent, self).__init__()
self.seed = seed
self.actor = ActorNetwork(input_size, output_size, name=f"Actor_Agent{agent_number}").to(device)
self.critic = CriticNetwork(input_size, output_size, name=f"Critic_Agent{agent_number}").to(device)
self.target_actor = ActorNetwork(input_size, output_size, name=f"Actor_Target_Agent{agent_number}").to(device)
self.target_critic = CriticNetwork(input_size, output_size, name=f"Critic_Target_Agent{agent_number}").to(device)
self.noise = OUActionNoise(mu=np.zeros(output_size))
self.tau = tau
self.epsilon = epsilon
self.epsilon_decay=epsilon_decay
self.gamma = gamma
self.clipgrad = clipgrad
self.actor_optimizer = optim.Adam(self.actor.parameters(), lr=lr_actor)
self.critic_optimizer = optim.Adam(self.critic.parameters(), lr=lr_critic, weight_decay=weight_decay)
def act(self, state, add_noise=True):
"""Returns actions for given state as per current policy."""
state = torch.from_numpy(state).float().unsqueeze(0).to(device) #.unsqueeze(0)
self.actor.eval()
with torch.no_grad():
action = self.actor(state).cpu().squeeze(0).data.numpy()
self.actor.train()
if add_noise:
action += self.noise.sample() * self.epsilon
return np.clip(action, -1, 1)
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.target_actor(next_states.to(device))
#set_trace()
Q_targets_next = self.target_critic(next_states.to(device), actions_next.to(device))
# 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(states, actions)
critic_loss = f.mse_loss(Q_expected, Q_targets)
# Minimize the loss
self.critic_optimizer.zero_grad()
critic_loss.backward()
clip_grad_norm_(self.critic.parameters(), self.clipgrad)
self.critic_optimizer.step()
# update actor
# Compute actor loss
actions_pred = self.actor(states)
actor_loss = -self.critic(states, actions_pred).mean()
# Minimize the loss
self.actor_optimizer.zero_grad()
actor_loss.backward()
#clip_grad_norm_(self.actor.parameters(), self.clipgrad)
self.actor_optimizer.step()
# update target networks
self.soft_update(self.critic, self.target_critic )
self.soft_update(self.actor, self.target_actor)
# update epsilon and noise
self.epsilon *= self.epsilon_decay
self.noise.reset()
def reset(self):
self.noise.reset()
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)
def save_models(self):
""" Save models weights """
self.actor.save_checkpoint()
self.critic.save_checkpoint()
self.target_actor.save_checkpoint()
self.target_critic.save_checkpoint()
def load_models(self):
""" Load models weights """
self.actor.load_checkpoint()
self.critic.load_checkpoint()
self.target_actor.load_checkpoint()
self.target_critic.load_checkpoint()
class Agent():
def __init__(self,
alpha,
beta,
input_dims,
tau,
env,
env_id,
gamma=0.99,
n_actions=2,
max_size=1000000,
layer_1_size=256,
layer_2_size=256,
batch_size=100,
reward_scale=2):
self.gamma = gamma
self.tau = tau
self.memory = ReplayBuffer(max_size, input_dims, n_actions)
self.batch_size = batch_size
self.n_actions = n_actions
self.scale = reward_scale
self.actor = ActorNetwork(alpha,
input_dims,
layer_1_size,
layer_2_size,
n_actions=n_actions,
name=env_id + '_actor',
max_action=env.action_space.high)
self.critic_1 = CriticNetwork(beta,
input_dims,
layer_1_size,
layer_2_size,
n_actions=n_actions,
name=env_id + '_critic_1')
self.critic_2 = CriticNetwork(beta,
input_dims,
layer_1_size,
layer_2_size,
n_actions=n_actions,
name=env_id + '_critic_2')
self.value = ValueNetwork(beta,
input_dims,
layer_1_size,
layer_2_size,
name=env_id + '_value')
self.target_value = ValueNetwork(beta,
input_dims,
layer_1_size,
layer_2_size,
name=env_id + '_target_value')
self.update_network_parameters(tau=1)
def choose_action(self, observation):
state = T.tensor([observation], dtype=T.float).to(self.actor.device)
actions, _ = self.actor.sample_normal(state, reparameterize=False)
return actions.cpu().detach().numpy()[0]
def remember(self, state, action, reward, state_, done):
self.memory.store_transitions(state, action, reward, state_, done)
def update_network_parameters(self, tau=None):
if tau is None:
tau = self.tau
value_params = self.value.named_parameters()
target_value_params = self.target_value.named_parameters()
value_state_dict = dict(value_params)
target_value_state_dict = dict(target_value_params)
for name in value_state_dict:
value_state_dict[name] = tau*value_state_dict[name].clone() \
+ (1-tau)*target_value_state_dict[name].clone()
self.target_value.load_state_dict(value_state_dict)
def save_models(self):
print('.... saving models ....')
self.actor.save_checkpoint()
self.value.save_checkpoint()
self.target_value.save_checkpoint()
self.critic_1.save_checkpoint()
self.critic_2.save_checkpoint()
def load_models(self):
print('.... loading models ....')
self.actor.load_checkpoint()
self.value.load_checkpoint()
self.target_value.load_checkpoint()
self.critic_1.load_checkpoint()
self.critic_2.load_checkpoint()
def learn(self):
if self.memory.mem_cntr < self.batch_size:
return
state, action, reward, state_, done =\
self.memory.sample_buffer(self.batch_size)
state = T.tensor(state, dtype=T.float).to(self.critic_1.device)
state_ = T.tensor(state_, dtype=T.float).to(self.critic_1.device)
action = T.tensor(action, dtype=T.float).to(self.critic_1.device)
reward = T.tensor(reward, dtype=T.float).to(self.critic_1.device)
done = T.tensor(done).to(self.critic_1.device)
value = self.value(state).view(-1)
value_ = self.target_value(state_).view(-1)
value_[done] = 0.0
actions, log_probs = self.actor.sample_normal(state,
reparameterize=False)
log_probs = log_probs.view(-1)
q1_new_policy = self.critic_1(state, actions)
q2_new_policy = self.critic_2(state, actions)
critic_value = T.min(q1_new_policy, q2_new_policy)
critic_value = critic_value.view(-1)
self.value.optimizer.zero_grad()
value_target = critic_value - log_probs
value_loss = 0.5 * F.mse_loss(value, value_target)
value_loss.backward(retain_graph=True)
self.value.optimizer.step()
actions, log_probs = self.actor.sample_normal(state,
reparameterize=True)
log_probs = log_probs.view(-1)
q1_new_policy = self.critic_1(state, actions)
q2_new_policy = self.critic_2(state, actions)
critic_value = T.min(q1_new_policy, q2_new_policy)
critic_value = critic_value.view(-1)
actor_loss = log_probs - critic_value
actor_loss = T.mean(actor_loss)
self.actor.optimizer.zero_grad()
actor_loss.backward(retain_graph=True)
self.actor.optimizer.step()
self.critic_1.optimizer.zero_grad()
self.critic_2.optimizer.zero_grad()
q_hat = self.scale * reward + self.gamma * value_
q1_old_policy = self.critic_1(state, action).view(-1)
q2_old_policy = self.critic_2(state, action).view(-1)
critic_1_loss = 0.5 * F.mse_loss(q1_old_policy, q_hat)
critic_2_loss = 0.5 * F.mse_loss(q2_old_policy, q_hat)
critic_loss = critic_1_loss + critic_2_loss
critic_loss.backward()
self.critic_1.optimizer.step()
self.critic_2.optimizer.step()
self.update_network_parameters()
class Agent():
def __init__(self,
alpha=0.0003,
beta=.0003,
input_dims=[8],
env=None,
gamma=.99,
n_actions=2,
max_size=1000000,
layer1_size=256,
layer2_size=256,
tau=.005,
batch_size=256,
reward_scale=2):
# reward scales depends on action convention for the environment
self.gamma = gamma
self.tau = tau
self.memory = ReplayBuffer(max_size, input_dims, n_actions)
self.batch_size = batch_size
self.n_actions = n_actions
# set up classes
self.actor = ActorNetwork(alpha,
input_dims,
max_action=env.action_space.high,
n_actions=n_actions,
name='actor')
self.critic1 = CriticNetwork(beta,
input_dims,
n_actions=n_actions,
name='critic_1')
self.critic2 = CriticNetwork(beta,
input_dims,
n_actions=n_actions,
name='critic_2')
self.value = ValueNetwork(beta, input_dims, name='value')
# target value
self.target_value = ValueNetwork(beta, input_dims, name='target_value')
self.scale = reward_scale
self.update_network_parameters(tau=1)
def choose_action(self, observation):
# here we turn into a tensor
state = T.tensor([observation]).to(self.actor.device).float()
# print(type(state))
actions, _ = self.actor.sample_normal(state, reparameterize=False)
return actions.cpu().detach().numpy()[0]
def remember(self, state, action, reward, new_state, done):
self.memory.store_transition(state, action, reward, new_state, done)
def update_network_parameters(self, tau=None):
if tau is None:
tau = self.tau
target_value_params = self.target_value.named_parameters()
value_params = self.value.named_parameters()
target_value_state_dict = dict(target_value_params)
value_state_dict = dict(value_params)
for name in value_state_dict:
value_state_dict[name] = tau * value_state_dict[name].clone() + (
1 - tau) * target_value_state_dict[name].clone()
self.target_value.load_state_dict(value_state_dict)
def save_models(self):
print("saving models:")
self.actor.save_checkpoint()
self.value.save_checkpoint()
self.target_value.save_checkpoint()
self.critic1.save_checkpoint()
self.critic2.save_checkpoint()
def load_models(self):
print("loading models:")
self.actor.load_checkpoint()
self.value.load_checkpoint()
self.target_value.load_checkpoint()
self.critic1.load_checkpoint()
self.critic2.load_checkpoint()
def learn(self):
# must fully load up memory, otherwise must keep learning
if self.memory.mem_cntr < self.batch_size:
return
state, action, reward, new_state, done = self.memory.sample_buffer(
self.batch_size)
reward = T.tensor(reward, dtype=T.float).to(self.actor.device)
done = T.tensor(done).to(self.actor.device)
state_ = T.tensor(new_state, dtype=T.float).to(self.actor.device)
state = T.tensor(state, dtype=T.float).to(self.actor.device)
action = T.tensor(action, dtype=T.float).to(self.actor.device)
value = self.value(state).view(-1)
value_ = self.target_value(state_).view(-1)
value_[done] = 0.0
actions, log_probs = self.actor.sample_normal(state,
reparameterize=False)
log_probs = log_probs.view(-1)
q1_new_policy = self.critic1.forward(state, actions)
q2_new_policy = self.critic2.forward(state, actions)
critic_value = T.min(q1_new_policy, q2_new_policy)
critic_value = critic_value.view(-1)
self.value.optimizer.zero_grad()
value_target = critic_value - log_probs
value_loss = .5 * F.mse_loss(value, value_target)
value_loss.backward(retain_graph=True)
self.value.optimizer.step()
actions, log_probs = self.actor.sample_normal(state,
reparameterize=True)
log_probs = log_probs.view(-1)
q1_new_policy = self.critic1.forward(state, actions)
q2_new_policy = self.critic2.forward(state, actions)
critic_value = T.min(q1_new_policy, q2_new_policy)
critic_value = critic_value.view(-1)
actor_loss = log_probs - critic_value
actor_loss = T.mean(actor_loss)
self.actor.optimizer.zero_grad()
actor_loss.backward(retain_graph=True)
self.actor.optimizer.step()
self.critic1.optimizer.zero_grad()
self.critic2.optimizer.zero_grad()
q_hat = self.scale * reward + self.gamma * value_
q1_old_policy = self.critic1.forward(state, action).view(-1)
q2_old_policy = self.critic2.forward(state, action).view(-1)
critic_1_loss = .5 * F.mse_loss(q1_old_policy, q_hat)
critic_2_loss = .5 * F.mse_loss(q2_old_policy, q_hat)
critic_loss = critic_1_loss + critic_2_loss
critic_loss.backward()
self.critic1.optimizer.step()
self.critic2.optimizer.step()
self.update_network_parameters()