class Agent():
def __init__(self,
alpha,
beta,
input_dims,
tau,
n_actions,
gamma=0.99,
max_size=50000,
fc1_dims=400,
fc2_dims=300,
batch_size=32):
self.gamma = gamma
self.tau = tau
self.batch_size = batch_size
self.alpha = alpha
self.beta = beta
self.memory = ReplayBuffer(max_size, input_dims, n_actions)
self.noise = OUActionNoise(mu=np.zeros(n_actions))
self.actor = ActorNetwork(alpha,
input_dims,
fc1_dims,
fc2_dims,
n_actions=n_actions,
name='actor')
self.critic = CriticNetwork(beta,
input_dims,
fc1_dims,
fc2_dims,
n_actions=n_actions,
name='critic')
self.target_actor = ActorNetwork(alpha,
input_dims,
fc1_dims,
fc2_dims,
n_actions=n_actions,
name='target_actor')
self.target_critic = CriticNetwork(beta,
input_dims,
fc1_dims,
fc2_dims,
n_actions=n_actions,
name='target_critic')
self.update_network_parameters(
tau=1) # for the first time target_actor and actor are same
def choose_action(self, observation):
self.actor.eval(
) # we are setting our actor network to eval mode because we have batch normalization layer
# and we dont want to calculate statistics for that layer at this step
state = T.tensor([observation], dtype=T.float).to(self.actor.device)
mu = self.actor.forward(state).to(self.actor.device)
mu_prime = mu + T.tensor(self.noise(), dtype=T.float).to(
self.actor.device)
self.actor.train()
return mu_prime.cpu().detach().numpy()[0]
def remember(self, state, action, reward, state_, done):
self.memory.store_transition(state, action, reward, state_, done)
def save_models(self):
self.actor.save_checkpoint()
self.target_actor.save_checkpoint()
self.critic.save_checkpoint()
self.target_critic.save_checkpoint()
def load_models(self):
self.actor.load_checkpoint()
self.target_actor.load_checkpoint()
self.critic.load_checkpoint()
self.target_critic.load_checkpoint()
def learn(self):
if self.memory.mem_cntr < self.batch_size:
return
states, actions, rewards, states_, done = self.memory.sample_buffer(
self.batch_size)
states = T.tensor(states, dtype=T.float).to(self.actor.device)
states_ = T.tensor(states_, dtype=T.float).to(self.actor.device)
actions = T.tensor(actions, dtype=T.float).to(self.actor.device)
rewards = T.tensor(rewards, dtype=T.float).to(self.actor.device)
done = T.tensor(done).to(self.actor.device)
target_actions = self.target_actor.forward(states_)
critic_value_ = self.target_critic.forward(states_, target_actions)
critic_value = self.critic.forward(states, actions)
critic_value_[done] = 0.0
critic_value_ = critic_value_.view(-1)
target = rewards + self.gamma * critic_value_
target = target.view(self.batch_size, 1)
self.critic.optimizer.zero_grad()
critic_loss = F.mse_loss(target, critic_value)
critic_loss.backward()
self.critic.optimizer.step()
self.actor.optimizer.zero_grad()
actor_loss = -self.critic.forward(states, self.actor.forward(states))
actor_loss = T.mean(actor_loss)
actor_loss.backward()
self.actor.optimizer.step()
self.update_network_parameters(
) # sending tau None so that local tau variable there takes the value of class tau variable
def update_network_parameters(self, tau=None):
if tau is None:
tau = self.tau
actor_params = self.actor.named_parameters()
critic_params = self.critic.named_parameters()
target_actor_params = self.target_actor.named_parameters()
target_critic_params = self.target_critic.named_parameters()
critic_state_dict = dict(critic_params)
actor_state_dict = dict(actor_params)
target_critic_state_dict = dict(target_critic_params)
target_actor_state_dict = dict(target_actor_params)
for name in critic_state_dict:
critic_state_dict[name] = tau * critic_state_dict[name].clone() + (
1 - tau) * target_critic_state_dict[name].clone()
for name in actor_state_dict:
actor_state_dict[name] = tau * actor_state_dict[name].clone() + (
1 - tau) * target_actor_state_dict[name].clone()
self.target_critic.load_state_dict(critic_state_dict)
self.target_actor.load_state_dict(actor_state_dict)
class Agent():
def __init__(self,
alpha=0.0003,
beta=0.0003,
input_dims=[8],
env=None,
gamma=0.99,
n_actions=2,
max_size=1000000,
tau=0.005,
layer1_size=256,
layer2_size=256,
batch_size=256,
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.actor = ActorNetwork(alpha,
input_dims,
n_actions=n_actions,
name='actor',
max_action=env.action_space.high)
self.critic_1 = CriticNetwork(beta,
input_dims,
n_actions=n_actions,
name='critic_1')
self.critic_2 = CriticNetwork(beta,
input_dims,
n_actions=n_actions,
name='critic_2')
self.value = ValueNetwork(beta, input_dims, name='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):
state = T.Tensor([observation]).to(self.actor.device)
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.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, 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.critic_1.forward(state, actions)
q2_new_policy = self.critic_2.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 = 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.forward(state, actions)
q2_new_policy = self.critic_2.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.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.forward(state, action).view(-1)
q2_old_policy = self.critic_2.forward(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()
#env = gym.make('InvertedPendulumPyBulletEnv-v0')
#env = gym.make('gym_lqr:lqr-stochastic-v0')
env = gym.make('gym_lqr:lqr-v0')
#env = gym.make('InvertedPendulum-v2')
#print(env.action_space.shape[0])
actor = ActorNetwork(0.0003, input_dims=env.observation_space.shape, \
n_actions=env.action_space.shape[0], max_action=env.action_space.high)
critic_1 = CriticNetwork(0.0003, input_dims=env.observation_space.shape, \
n_actions=env.action_space.shape[0], name='critic_1')
critic_2 = CriticNetwork(0.0003, input_dims=env.observation_space.shape, \
n_actions=env.action_space.shape[0], name='critic_2')
ActorNetwork.load_checkpoint(actor)
critic_1.load_checkpoint()
critic_2.load_checkpoint()
# Load optimal P
env.set_P(np.load('tmp/sac/optimal_P.npy'))
# Create States and Actions
states = np.expand_dims(
np.expand_dims(np.arange(-100, 100, 5, dtype=np.float32), -1), -1)
actions = np.expand_dims(
np.expand_dims(np.arange(-10, 10, 0.5, dtype=np.float32), -1), -1)
# states = []
# actions = []
q_env = []
q_network = []
#print(states)
class Agent_2():
def __init__(self,
alpha=0.00005,
beta=0.00005,
input_dims=5,
env=None,
gamma=0.99,
n_actions=2,
max_size=1000000,
tau=0.005,
layer1_size=256,
layer2_size=256,
batch_size=256,
reward_scale=2):
self.gamma = 0.99
self.tau = tau
self.memeory = ReplayBuffer(max_size, input_dims, n_actions)
self.batch_size = batch_size
self.n_actions = n_actions
latent_dims = 10
self.actor = ActorNetwork_2(alpha,
latent_dims,
env.action_space.high,
n_actions=n_actions)
self.critic_1 = CriticNetwork(beta,
latent_dims,
n_actions,
name='critic_det_1')
self.critic_2 = CriticNetwork(beta,
latent_dims,
n_actions,
name='critic__det_2')
self.value = ValueNetwork(beta, latent_dims, name='value_det')
self.target_value = ValueNetwork(beta,
latent_dims,
name='target_value_det')
self.VAE = LinearVAE()
self.scale = reward_scale
self.update_network_parameters(tau=1)
def choose_action(self, observation):
state = torch.Tensor([observation]).to(self.actor.device)
state_latent = self.VAE.sample_normal(state)
actions = self.actor(state_latent)
return actions.cpu().detach().numpy()[0]
def remember(self, state, action, reward, new_state, done):
self.memeory.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_dict = dict(target_value_params)
value_dict = dict(value_params)
for name in target_value_dict:
target_value_dict[name] = tau*value_dict[name].clone() + \
(1-tau)*target_value_dict[name].clone()
self.target_value.load_state_dict(target_value_dict)
def save_models(self):
print('... saving models ...')
self.actor.save_checkpoint()
self.critic_1.save_checkpoint()
self.critic_2.save_checkpoint()
self.value.save_checkpoint()
self.target_value.save_checkpoint()
def load_models(self):
print('... loading models ...')
self.actor.load_checkpoint()
self.critic_1.load_checkpoint()
self.critic_2.load_checkpoint()
self.value.load_checkpoint()
self.target_value.load_checkpoint()
def learn(self):
if self.memeory.mem_cntr < self.batch_size:
return
states, new_states, actions, rewards, dones = self.memeory.sample_buffer(
self.batch_size)
states = torch.tensor(states, dtype=torch.float).to(self.actor.device)
new_states = torch.tensor(new_states,
dtype=torch.float).to(self.actor.device)
actions = torch.tensor(actions,
dtype=torch.float).to(self.actor.device)
rewards = torch.tensor(rewards,
dtype=torch.float).to(self.actor.device)
dones = torch.tensor(dones).to(self.actor.device)
# Train VAE with KL divergence + reconstruction_loss + log_probs
reconstruction, mu, logvar, log_probs = self.VAE(states)
KLD = -0.5 * torch.sum(1 + logvar - mu.pow(2) - logvar.exp())
reconstruction_loss = F.mse_loss(reconstruction, states)
final_loss = KLD + reconstruction_loss
self.VAE.optimizer.zero_grad()
final_loss.backward(retain_graph=True)
self.VAE.optimizer.step()
latent_states = self.VAE.sample_normal(states)
states_value = self.value(latent_states).view(-1)
new_latent_states = self.VAE.sample_normal(new_states)
new_states_value = self.target_value(new_latent_states).view(-1)
new_states_value[dones] = 0.0
action = self.actor(latent_states)
q1_new_policy = self.critic_1(latent_states, action)
q2_new_policy = self.critic_2(latent_states, action)
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
value_loss = 0.5 * F.mse_loss(states_value, value_target)
value_loss.backward(retain_graph=True)
self.value.optimizer.step()
actor_loss = -critic_value
actor_loss = torch.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 * rewards + self.gamma * new_states_value
q1_old_policy = self.critic_1(latent_states, actions).view(-1)
q2_old_policy = self.critic_2(latent_states, actions).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.critic_1.optimizer.step()
self.critic_2.optimizer.step()
self.update_network_parameters()
return final_loss, value_loss, actor_loss, critic_loss
class Agent_sm():
def __init__(self,
alpha=0.0003,
beta=0.0003,
input_dims=8,
env=None,
gamma=0.99,
n_actions=2,
max_size=1000000,
tau=0.005,
layer1_size=256,
layer2_size=256,
batch_size=256,
reward_scale=2):
self.gamma = 0.99
self.tau = tau
self.memeory = ReplayBuffer(max_size, input_dims, n_actions)
self.batch_size = batch_size
self.n_actions = n_actions
self.actor = ActorNetwork(alpha,
input_dims,
env.action_space.high,
n_actions=n_actions)
self.critic_1 = CriticNetwork(beta,
input_dims,
n_actions,
name='critic_1')
self.critic_2 = CriticNetwork(beta,
input_dims,
n_actions,
name='critic_2')
self.value = ValueNetwork(beta, input_dims, name='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):
state = torch.Tensor([observation]).to(self.actor.device)
actions, _ = self.actor.sample_normal(state, reparameterize=False)
return actions.cpu().detach().numpy()[0]
def remember(self, state, action, reward, new_state, done):
self.memeory.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_dict = dict(target_value_params)
value_dict = dict(value_params)
for name in target_value_dict:
target_value_dict[name] = tau*value_dict[name].clone() + \
(1-tau)*target_value_dict[name].clone()
self.target_value.load_state_dict(target_value_dict)
def save_models(self):
print('... saving models ...')
self.actor.save_checkpoint()
self.critic_1.save_checkpoint()
self.critic_2.save_checkpoint()
self.value.save_checkpoint()
self.target_value.save_checkpoint()
def load_models(self):
print('... loading models ...')
self.actor.load_checkpoint()
self.critic_1.load_checkpoint()
self.critic_2.load_checkpoint()
self.value.load_checkpoint()
self.target_value.load_checkpoint()
def learn(self):
if self.memeory.mem_cntr < self.batch_size:
return
states, new_states, actions, rewards, dones = self.memeory.sample_buffer(
self.batch_size)
states = torch.tensor(states, dtype=torch.float).to(self.actor.device)
new_states = torch.tensor(new_states,
dtype=torch.float).to(self.actor.device)
actions = torch.tensor(actions,
dtype=torch.float).to(self.actor.device)
rewards = torch.tensor(rewards,
dtype=torch.float).to(self.actor.device)
dones = torch.tensor(dones).to(self.actor.device)
states_value = self.value(states).view(-1)
new_states_value = self.target_value(new_states).view(-1)
new_states_value[dones] = 0.0
action, log_probs = self.actor.sample_normal(states,
reparameterize=False)
log_probs = log_probs.view(-1)
q1_new_policy = self.critic_1(states, action)
q2_new_policy = self.critic_2(states, action)
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(states_value, value_target)
value_loss.backward(retain_graph=True)
self.value.optimizer.step()
action, log_probs = self.actor.sample_normal(states,
reparameterize=True)
log_probs = log_probs.view(-1)
q1_new_policy = self.critic_1(states, action)
q2_new_policy = self.critic_2(states, action)
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.critic_1.optimizer.zero_grad()
self.critic_2.optimizer.zero_grad()
q_hat = self.scale * rewards + self.gamma * new_states_value
q1_old_policy = self.critic_1(states, actions).view(-1)
q2_old_policy = self.critic_2(states, actions).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.critic_1.optimizer.step()
self.critic_2.optimizer.step()
self.update_network_parameters()
# value_loss = value_loss.cpu().detach().numpy()[0]
# actor_loss = actor_loss.cpu().detach().numpy()[0]
# critic_loss = critic_loss.cpu().detach().numpy()[0]
return 0, value_loss, actor_loss, critic_loss
def learn_sm(self, sm_reg=1):
if self.memeory.mem_cntr < self.batch_size:
return
states, new_states, actions, rewards, dones = self.memeory.sample_buffer(
self.batch_size)
states = torch.tensor(states, dtype=torch.float).to(self.actor.device)
new_states = torch.tensor(new_states,
dtype=torch.float).to(self.actor.device)
actions = torch.tensor(actions,
dtype=torch.float).to(self.actor.device)
rewards = torch.tensor(rewards,
dtype=torch.float).to(self.actor.device)
dones = torch.tensor(dones).to(self.actor.device)
states_value = self.value(states).view(-1)
new_states_value = self.target_value(new_states).view(-1)
new_states_value[dones] = 0.0
# action, log_probs = self.actor.sample_normal(states, reparameterize=False)
# log_probs = log_probs.view(-1)
# q1_new_policy = self.critic_1(states, action)
# q2_new_policy = self.critic_2(states, action)
# 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(states_value, value_target)
# value_loss.backward(retain_graph=True)
# self.value.optimizer.step()
# action, log_probs = self.actor.sample_normal(states, reparameterize=True)
action, _ = self.actor.sample_normal(states, reparameterize=True)
# log_probs = log_probs.view(-1)
q1_new_policy = self.critic_1(states, action)
q2_new_policy = self.critic_2(states, action)
critic_value = torch.min(q1_new_policy, q2_new_policy)
critic_value = critic_value.view(-1)
# sample actions for next batch states
action_next, _ = self.actor.sample_normal(new_states,
reparameterize=True)
q1_new_policy = self.critic_1(new_states, action_next)
q2_new_policy = self.critic_2(new_states, action_next)
critic_value_next = torch.min(q1_new_policy, q2_new_policy)
critic_value_next = critic_value.view(-1)
# actor_loss = log_probs - critic_value
actor_loss = -(critic_value + critic_value_next) + sm_reg * F.mse_loss(
action, action_next)
actor_loss = torch.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*rewards + self.gamma*new_states_value
# q1_old_policy = self.critic_1(states, actions).view(-1)
# q2_old_policy = self.critic_2(states, actions).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.critic_1.optimizer.step()
# self.critic_2.optimizer.step()
# self.update_network_parameters()
return 0, 0, actor_loss, 0
class Agent:
def __init__(self,
actor_dims,
critic_dims,
n_actions,
n_agents,
agent_idx,
chkpt_dir,
alpha=0.01,
beta=0.01,
fc1=64,
fc2=64,
gamma=0.95,
tau=0.01):
"""
Args:
actor_dims:
critic_dims:
n_actions: number of actions
n_agents:
agent_idx: agent index
chkpt_dir: checkpoint directory
alpha: learning rate
beta: learning rate
fc1:
fc2:
gamma: discount factor
tau: soft update parameter
"""
self.gamma = gamma
self.tau = tau
self.n_actions = n_actions
self.agent_name = 'agent_%s' % agent_idx
# e.g., name = agent_1_actor
self.actor = ActorNetwork(alpha,
actor_dims,
fc1,
fc2,
n_actions,
chkpt_dir=chkpt_dir,
name=self.agent_name + '_actor')
self.critic = CriticNetwork(beta,
critic_dims,
fc1,
fc2,
n_agents,
n_actions,
chkpt_dir=chkpt_dir,
name=self.agent_name + '_critic')
self.target_actor = ActorNetwork(alpha,
actor_dims,
fc1,
fc2,
n_actions,
chkpt_dir=chkpt_dir,
name=self.agent_name +
'_target_actor')
self.target_critic = CriticNetwork(beta,
critic_dims,
fc1,
fc2,
n_agents,
n_actions,
chkpt_dir=chkpt_dir,
name=self.agent_name +
'_target_critic')
# initially target networks and networks have the same parameters
self.update_network_parameters(tau=1)
def choose_action(self, observation):
"""
Args:
observation:
Returns: action w.r.t. the current policy and exploration
"""
state = T.tensor([observation], dtype=T.float).to(self.actor.device)
# action of current policy
actions = self.actor.forward(state)
# exploration (0.1 is the parameter of the exploration)
noise = 0.1 * T.rand(self.n_actions).to(self.actor.device)
# print(f"action={action}, noise={noise}")
action = actions + noise
# action = actions
return action.detach().cpu().numpy()[0]
def update_network_parameters(self, tau=None):
# use default tau if nothing is input
if tau is None:
tau = self.tau
target_actor_params = self.target_actor.named_parameters()
actor_params = self.actor.named_parameters()
# soft update of target networks
target_actor_state_dict = dict(target_actor_params)
actor_state_dict = dict(actor_params)
for name in actor_state_dict:
actor_state_dict[name] = tau * actor_state_dict[name].clone() + \
(1 - tau) * target_actor_state_dict[
name].clone()
self.target_actor.load_state_dict(actor_state_dict)
target_critic_params = self.target_critic.named_parameters()
critic_params = self.critic.named_parameters()
target_critic_state_dict = dict(target_critic_params)
critic_state_dict = dict(critic_params)
for name in critic_state_dict:
critic_state_dict[name] = tau * critic_state_dict[name].clone() + \
(1 - tau) * target_critic_state_dict[
name].clone()
self.target_critic.load_state_dict(critic_state_dict)
def save_models(self):
self.actor.save_checkpoint()
self.target_actor.save_checkpoint()
self.critic.save_checkpoint()
self.target_critic.save_checkpoint()
def load_models(self):
self.actor.load_checkpoint()
self.target_actor.load_checkpoint()
self.critic.load_checkpoint()
self.target_critic.load_checkpoint()
class Agent():
def __init__(self, alpha=0.0003, beta=0.0003, input_dims=[8],
env=None, gamma=0.99, n_actions=2, max_size=1000000, tau=0.005,
layer1_size=256, layer2_size=256, batch_size=256, 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.actor = ActorNetwork(alpha, input_dims, n_actions=n_actions,
name='actor', max_action=env.action_space.high)
self.critic_1 = CriticNetwork(beta, input_dims, n_actions=n_actions,
name='critic_1')
self.critic_2 = CriticNetwork(beta, input_dims, n_actions=n_actions,
name='critic_2')
self.value = ValueNetwork(beta, input_dims, name='value')
self.target_value = ValueNetwork(beta, input_dims, name='target_value')
self.scale = reward_scale
self.update_network_parameters(tau=1) #sets the parameters of Target-network equals to the
#values of Target-network in the beginning
def choose_action(self, observation):
state = T.Tensor([observation]).to(self.actor.device)
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: #different copies: exact VS soft
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.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, new_state, done = \ #takes the batch
self.memory.sample_buffer(self.batch_size)
reward = T.tensor(reward, dtype=T.float).to(self.actor.device) #trasforms into tensors
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 #####_ sta usando 0 per dire True? @15, 17
actions, log_probs = self.actor.sample_normal(state, reparameterize=False) #takes the lower Q-values from 2 Critics to the Critic
log_probs = log_probs.view(-1)
q1_new_policy = self.critic_1.forward(state, actions)
q2_new_policy = self.critic_2.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 ####_ Perchè non prende semplicemente il critic_value?
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.forward(state, actions)
q2_new_policy = self.critic_2.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.critic_1.optimizer.zero_grad()
self.critic_2.optimizer.zero_grad()
q_hat = self.scale * reward + self.gamma * value_ #The scaling factor takes into account the entropy and encourage exploration
q1_old_policy = self.critic_1.forward(state, action).view(-1)
q2_old_policy = self.critic_2.forward(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(object):
def __init__(self,
alpha,
beta,
input_dims,
action_bound,
tau,
env,
gamma=0.99,
n_actions=2,
max_size=1000000,
layer1_size=400,
layer2_size=300,
batch_size=64):
self.gamma = gamma
self.tau = tau
self.memory = ReplayBuffer(max_size, input_dims, n_actions)
self.batch_size = batch_size
self.action_bound = action_bound
self.actor = ActorNetwork(alpha,
input_dims,
layer1_size,
layer2_size,
n_actions=n_actions,
name='Actor')
self.critic = CriticNetwork(beta,
input_dims,
layer1_size,
layer2_size,
n_actions=n_actions,
name='Critic')
self.target_actor = ActorNetwork(alpha,
input_dims,
layer1_size,
layer2_size,
n_actions=n_actions,
name='TargetActor')
self.target_critic = CriticNetwork(beta,
input_dims,
layer1_size,
layer2_size,
n_actions=n_actions,
name='TargetCritic')
self.noise = OUActionNoise(mu=np.zeros(n_actions))
self.update_network_parameters(tau=1)
def choose_action(self, observation):
self.actor.eval()
observation = T.tensor(observation,
dtype=T.float).to(self.actor.device)
mu = self.actor.forward(observation).to(self.actor.device)
mu_prime = mu + T.tensor(self.noise(), dtype=T.float).to(
self.actor.device)
self.actor.train()
return (mu_prime * T.tensor(self.action_bound)).cpu().detach().numpy()
def remember(self, state, action, reward, new_state, done):
self.memory.store_transition(state, action, reward, new_state, done)
def learn(self):
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.critic.device)
done = T.tensor(done).to(self.critic.device)
new_state = T.tensor(new_state, dtype=T.float).to(self.critic.device)
action = T.tensor(action, dtype=T.float).to(self.critic.device)
state = T.tensor(state, dtype=T.float).to(self.critic.device)
self.target_actor.eval()
self.target_critic.eval()
self.critic.eval()
target_actions = self.target_actor.forward(new_state)
critic_value_ = self.target_critic.forward(new_state, target_actions)
critic_value = self.critic.forward(state, action)
target = []
for j in range(self.batch_size):
target.append(reward[j] + self.gamma * critic_value_[j] * done[j])
target = T.tensor(target).to(self.critic.device)
target = target.view(self.batch_size, 1)
self.critic.train()
self.critic.optimizer.zero_grad()
critic_loss = F.mse_loss(target, critic_value)
critic_loss.backward()
self.critic.optimizer.step()
self.critic.eval()
self.actor.optimizer.zero_grad()
mu = self.actor.forward(state)
self.actor.train()
actor_loss = -self.critic.forward(state, mu)
actor_loss = T.mean(actor_loss)
actor_loss.backward()
self.actor.optimizer.step()
self.update_network_parameters()
def update_network_parameters(self, tau=None):
if tau is None:
tau = self.tau
actor_params = self.actor.named_parameters()
critic_params = self.critic.named_parameters()
target_actor_params = self.target_actor.named_parameters()
target_critic_params = self.target_critic.named_parameters()
critic_state_dict = dict(critic_params)
actor_state_dict = dict(actor_params)
target_critic_dict = dict(target_critic_params)
target_actor_dict = dict(target_actor_params)
for name in critic_state_dict:
critic_state_dict[name] = tau*critic_state_dict[name].clone() + \
(1-tau)*target_critic_dict[name].clone()
self.target_critic.load_state_dict(critic_state_dict)
for name in actor_state_dict:
actor_state_dict[name] = tau*actor_state_dict[name].clone() + \
(1-tau)*target_actor_dict[name].clone()
self.target_actor.load_state_dict(actor_state_dict)
"""
#Verify that the copy assignment worked correctly
target_actor_params = self.target_actor.named_parameters()
target_critic_params = self.target_critic.named_parameters()
critic_state_dict = dict(target_critic_params)
actor_state_dict = dict(target_actor_params)
print('\nActor Networks', tau)
for name, param in self.actor.named_parameters():
print(name, T.equal(param, actor_state_dict[name]))
print('\nCritic Networks', tau)
for name, param in self.critic.named_parameters():
print(name, T.equal(param, critic_state_dict[name]))
input()
"""
def save_models(self):
self.actor.save_checkpoint()
self.target_actor.save_checkpoint()
self.critic.save_checkpoint()
self.target_critic.save_checkpoint()
def load_models(self):
self.actor.load_checkpoint()
self.target_actor.load_checkpoint()
self.critic.load_checkpoint()
self.target_critic.load_checkpoint()
def check_actor_params(self):
current_actor_params = self.actor.named_parameters()
current_actor_dict = dict(current_actor_params)
original_actor_dict = dict(self.original_actor.named_parameters())
original_critic_dict = dict(self.original_critic.named_parameters())
current_critic_params = self.critic.named_parameters()
current_critic_dict = dict(current_critic_params)
print('Checking Actor parameters')
for param in current_actor_dict:
print(
param,
T.equal(original_actor_dict[param], current_actor_dict[param]))
print('Checking critic parameters')
for param in current_critic_dict:
print(
param,
T.equal(original_critic_dict[param],
current_critic_dict[param]))
input()
class Agent:
def __init__(self,
actor_dims,
critic_dims,
n_actions,
n_agents,
agent_idx,
chkpt_dir,
alpha=0.01,
beta=0.01,
fc1=64,
fc2=64,
gamma=0.95,
tau=0.01):
self.gamma = gamma
self.tau = tau
self.n_actions = n_actions
self.agent_name = 'agent_%s' % agent_idx
self.actor = ActorNetwork(alpha,
actor_dims,
fc1,
fc2,
n_actions,
chkpt_dir=chkpt_dir,
name=self.agent_name + '_actor')
self.critic = CriticNetwork(beta,
critic_dims,
fc1,
fc2,
n_agents,
n_actions,
chkpt_dir=chkpt_dir,
name=self.agent_name + '_critic')
self.target_actor = ActorNetwork(alpha,
actor_dims,
fc1,
fc2,
n_actions,
chkpt_dir=chkpt_dir,
name=self.agent_name +
'_target_actor')
self.target_critic = CriticNetwork(beta,
critic_dims,
fc1,
fc2,
n_agents,
n_actions,
chkpt_dir=chkpt_dir,
name=self.agent_name +
'_target_critic')
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.forward(state)
noise = T.rand(self.n_actions).to(self.actor.device)
action = actions + noise
return action.detach().cpu().numpy()[0]
def update_network_parameters(self, tau=None):
if tau is None:
tau = self.tau
target_actor_params = self.target_actor.named_parameters()
actor_params = self.actor.named_parameters()
target_actor_state_dict = dict(target_actor_params)
actor_state_dict = dict(actor_params)
for name in actor_state_dict:
actor_state_dict[name] = tau*actor_state_dict[name].clone() + \
(1-tau)*target_actor_state_dict[name].clone()
self.target_actor.load_state_dict(actor_state_dict)
target_critic_params = self.target_critic.named_parameters()
critic_params = self.critic.named_parameters()
target_critic_state_dict = dict(target_critic_params)
critic_state_dict = dict(critic_params)
for name in critic_state_dict:
critic_state_dict[name] = tau*critic_state_dict[name].clone() + \
(1-tau)*target_critic_state_dict[name].clone()
self.target_critic.load_state_dict(critic_state_dict)
def save_models(self):
self.actor.save_checkpoint()
self.target_actor.save_checkpoint()
self.critic.save_checkpoint()
self.target_critic.save_checkpoint()
def load_models(self):
self.actor.load_checkpoint()
self.target_actor.load_checkpoint()
self.critic.load_checkpoint()
self.target_critic.load_checkpoint()
class Agent():
def __init__(self,
alpha=0.0003,
beta=0.0003,
input_dims=8,
env=None,
gamma=0.99,
n_actions=2,
max_size=1000000,
tau=0.005,
layer1_size=256,
layer2_size=256,
batch_size=256,
reward_scale=2):
self.gamma = 0.99
self.tau = tau
self.memeory = ReplayBuffer(max_size, input_dims, n_actions)
self.batch_size = batch_size
self.n_actions = n_actions
self.actor = ActorNetwork(alpha,
input_dims,
env.action_space.high,
n_actions=n_actions)
self.critic_1 = CriticNetwork(beta,
input_dims,
n_actions,
name='critic_1')
self.critic_2 = CriticNetwork(beta,
input_dims,
n_actions,
name='critic_2')
self.value = ValueNetwork(beta, input_dims, name='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):
state = torch.Tensor([observation]).to(self.actor.device)
actions, _ = self.actor.sample_normal(state, reparameterize=False)
return actions.cpu().detach().numpy()[0]
def remember(self, state, action, reward, new_state, done):
self.memeory.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_dict = dict(target_value_params)
value_dict = dict(value_params)
for name in target_value_dict:
target_value_dict[name] = tau*value_dict[name].clone() + \
(1-tau)*target_value_dict[name].clone()
self.target_value.load_state_dict(target_value_dict)
def save_models(self):
print('... saving models ...')
self.actor.save_checkpoint()
self.critic_1.save_checkpoint()
self.critic_2.save_checkpoint()
self.value.save_checkpoint()
self.target_value.save_checkpoint()
def load_models(self):
print('... loading models ...')
self.actor.load_checkpoint()
self.critic_1.load_checkpoint()
self.critic_2.load_checkpoint()
self.value.load_checkpoint()
self.target_value.load_checkpoint()
def learn(self):
if self.memeory.mem_cntr < self.batch_size:
return
states, new_states, actions, rewards, dones = self.memeory.sample_buffer(
self.batch_size)
states = torch.tensor(states, dtype=torch.float).to(self.actor.device)
new_states = torch.tensor(new_states,
dtype=torch.float).to(self.actor.device)
actions = torch.tensor(actions,
dtype=torch.float).to(self.actor.device)
rewards = torch.tensor(rewards,
dtype=torch.float).to(self.actor.device)
dones = torch.tensor(dones).to(self.actor.device)
states_value = self.value(states).view(-1)
new_states_value = self.target_value(new_states).view(-1)
new_states_value[dones] = 0.0
action, log_probs = self.actor.sample_normal(states,
reparameterize=False)
log_probs = log_probs.view(-1)
q1_new_policy = self.critic_1(states, action)
q2_new_policy = self.critic_2(states, action)
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(states_value, value_target)
value_loss.backward(retain_graph=True)
self.value.optimizer.step()
action, log_probs = self.actor.sample_normal(states,
reparameterize=True)
log_probs = log_probs.view(-1)
q1_new_policy = self.critic_1(states, action)
q2_new_policy = self.critic_2(states, action)
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.critic_1.optimizer.zero_grad()
self.critic_2.optimizer.zero_grad()
q_hat = self.scale * rewards + self.gamma * new_states_value
q1_old_policy = self.critic_1(states, actions).view(-1)
q2_old_policy = self.critic_2(states, actions).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.critic_1.optimizer.step()
self.critic_2.optimizer.step()
self.update_network_parameters()
return value_loss, actor_loss, critic_loss
def train_on_env(self, env):
rewards = []
done = False
observation = env.reset()
while not done:
action = self.choose_action(observation)
observation_, reward, done, _ = env.step(action)
self.remember(observation, action, reward, observation_, done)
#if not load_checkpoints:
self.learn()
observation = observation_
rewards.append(reward)
return np.sum(rewards)
def generate_session(self, env, t_max=1000):
states, traj_probs, actions, rewards = [], [], [], []
s = env.reset()
q_t = 0
for t in range(t_max):
state = torch.Tensor([s]).to(self.actor.device)
action, log_probs = self.actor.sample_normal(state,
reparameterize=False)
action = action.cpu().detach().numpy()[0]
new_s, r, done, info = env.step(action)
log_probs = log_probs.cpu().detach().numpy()[0]
#q_t *= probs
q_t += log_probs[0]
states.append(s.tolist())
traj_probs.append(q_t)
actions.append(action[0])
rewards.append(r)
s = new_s
if done:
break
return np.array(states), np.array(traj_probs), np.array(
actions), np.array(rewards)
class Agent():
def __init__(self, input_dims, env, n_actions):
self.memory = ReplayBuffer(input_dims)
self.n_actions = n_actions
self.actor_nn = ActorNetwork(input_dims,
n_actions=n_actions,
name=Constants.env_id + '_actor',
max_action=env.action_space.n)
self.critic_local_1_nn = CriticNetwork(input_dims,
n_actions=n_actions,
name=Constants.env_id +
'_critic_local_1')
self.critic_local_2_nn = CriticNetwork(input_dims,
n_actions=n_actions,
name=Constants.env_id +
'_critic_local_2')
self.critic_target_1_nn = CriticNetwork(input_dims,
n_actions=n_actions,
name=Constants.env_id +
'_critic_target_1')
self.critic_target_2_nn = CriticNetwork(input_dims,
n_actions=n_actions,
name=Constants.env_id +
'_critic_target_2')
def choose_action(self, observation):
state = T.Tensor([observation]).to(Constants.device)
_, max_probability_action = self.actor_nn.sample_action(state)
return max_probability_action
def remember(self, state, action, reward, new_state, done):
self.memory.store_transition(state, action, reward, new_state, done)
def learn(self):
if self.memory.mem_cntr < Hyper.batch_size:
return
state, action, reward, next_state, done = self.memory.sample_buffer()
reward = T.tensor(reward, dtype=T.float).to(Constants.device)
done = T.tensor(done).to(Constants.device)
next_state = T.tensor(next_state, dtype=T.float).to(Constants.device)
state = T.tensor(state, dtype=T.float).to(Constants.device)
action = T.tensor(action, dtype=T.float).to(Constants.device)
(action_probabilities,
log_action_probabilities), _ = self.actor_nn.sample_action(next_state)
with T.no_grad():
action_logits1 = self.critic_target_1_nn(next_state)
q1_new_policy = T.argmax(action_logits1, dim=1, keepdim=True)
action_logits2 = self.critic_target_2_nn(next_state)
q2_new_policy = T.argmax(action_logits2, dim=1, keepdim=True)
q_value = T.min(q1_new_policy, q2_new_policy)
min_qf_next_target = action_probabilities * (
q_value - Hyper.alpha * log_action_probabilities)
min_qf_next_target_sum = min_qf_next_target.sum(
dim=1).unsqueeze(-1)
not_done = (1.0 - done * 1).unsqueeze(-1)
next_q_value = reward.unsqueeze(
-1) + not_done * Hyper.gamma * (min_qf_next_target_sum)
action_logits1 = self.critic_local_1_nn(state).gather(1, action.long())
q_value1 = action_logits1.sum(dim=1).unsqueeze(-1)
action_logits2 = self.critic_local_2_nn(state).gather(1, action.long())
q_value2 = action_logits2.sum(dim=1).unsqueeze(-1)
self.critic_local_1_nn.optimizer.zero_grad()
self.critic_local_2_nn.optimizer.zero_grad()
critic_1_loss = F.mse_loss(q_value1, next_q_value)
critic_2_loss = F.mse_loss(q_value2, next_q_value)
critic_1_loss.backward()
critic_2_loss.backward()
self.critic_local_1_nn.optimizer.step()
self.critic_local_2_nn.optimizer.step()
(action_probabilities,
log_action_probabilities), _ = self.actor_nn.sample_action(state)
# -------------------------------------------------------------------------------------------
# Calculates the loss for the actor. This loss includes the additional entropy term
# CHANGE0003 Soft state-value where actions are discrete
self.actor_nn.optimizer.zero_grad()
action_logits1 = self.critic_target_1_nn(state)
q1_new_policy = T.argmax(action_logits1, dim=1, keepdim=True)
action_logits2 = self.critic_target_2_nn(state)
q2_new_policy = T.argmax(action_logits2, dim=1, keepdim=True)
q_value = T.min(q1_new_policy, q2_new_policy)
inside_term = Hyper.alpha * log_action_probabilities - q_value
policy_loss = (action_probabilities * inside_term).sum(dim=1).mean()
policy_loss.backward(retain_graph=True)
self.actor_nn.optimizer.step()
self.update_q_weights()
def update_q_weights(self):
local_1_parameters = self.critic_local_1_nn.named_parameters()
local_2_parameters = self.critic_local_2_nn.named_parameters()
target_1_parameters = self.critic_target_1_nn.named_parameters()
target_2_parameters = self.critic_target_2_nn.named_parameters()
self.update_network_parameters_line(target_1_parameters,
local_1_parameters, Hyper.tau)
self.update_network_parameters_line(target_2_parameters,
local_2_parameters, Hyper.tau)
def update_network_parameters_line(self, target_params, local_params, tau):
for target_param, local_param in zip(target_params, local_params):
target_param[1].data.copy_(tau * local_param[1].data +
(1.0 - tau) * target_param[1].data)
def save_models(self):
print('.... saving models ....')
self.actor_nn.save_checkpoint()
self.critic_local_1_nn.save_checkpoint()
self.critic_local_2_nn.save_checkpoint()
self.critic_target_1_nn.save_checkpoint()
self.critic_target_2_nn.save_checkpoint()
def load_models(self):
print('.... loading models ....')
self.actor_nn.load_checkpoint()
self.critic_local_1_nn.load_checkpoint()
self.critic_local_2_nn.load_checkpoint()
self.critic_target_1_nn.load_checkpoint()
self.critic_target_2_nn.load_checkpoint()
class Agent():
def __init__(self,
alpha,
beta,
input_dims,
tau,
env,
action_bound,
gamma=0.99,
update_actor_interval=2,
warmup=1000,
n_actions=2,
max_size=1000000,
layer1_size=400,
layer2_size=300,
batch_size=100,
noise=0.1):
self.gamma = gamma
self.tau = tau
self.max_action = env.action_space.high
self.min_action = env.action_space.low
self.memory = ReplayBuffer(max_size, input_dims, n_actions)
self.batch_size = batch_size
self.learn_step_cntr = 0
self.time_step = 0
self.warmup = warmup
self.n_actions = n_actions
self.update_actor_iter = update_actor_interval
self.action_bound = action_bound
self.actor = ActorNetwork(alpha,
input_dims,
layer1_size,
layer2_size,
n_actions=n_actions,
name='actor')
self.critic_1 = CriticNetwork(beta,
input_dims,
layer1_size,
layer2_size,
n_actions=n_actions,
name='critic_1')
self.critic_2 = CriticNetwork(beta,
input_dims,
layer1_size,
layer2_size,
n_actions=n_actions,
name='critic_2')
self.target_actor = ActorNetwork(alpha,
input_dims,
layer1_size,
layer2_size,
n_actions=n_actions,
name='target_actor')
self.target_critic_1 = CriticNetwork(beta,
input_dims,
layer1_size,
layer2_size,
n_actions=n_actions,
name='target_critic_1')
self.target_critic_2 = CriticNetwork(beta,
input_dims,
layer1_size,
layer2_size,
n_actions=n_actions,
name='target_critic_2')
self.noise = noise
self.update_network_parameters(tau=1)
def choose_action(self, observation):
if self.time_step < self.warmup:
mu = T.tensor(
np.random.normal(scale=self.noise, size=(self.n_actions, )))
else:
state = T.tensor(observation, dtype=T.float).to(self.actor.device)
mu = self.actor.forward(state).to(self.actor.device)
mu_prime = mu + T.tensor(np.random.normal(scale=self.noise),
dtype=T.float).to(self.actor.device)
mu_prime = T.clamp(mu_prime, self.min_action[0], self.max_action[0])
self.time_step += 1
return (mu_prime * T.tensor(self.action_bound)).cpu().detach().numpy()
def remember(self, state, action, reward, new_state, done):
self.memory.store_transition(state, action, reward, new_state, done)
def learn(self):
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.critic_1.device)
done = T.tensor(done).to(self.critic_1.device)
state_ = T.tensor(new_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)
target_actions = self.target_actor.forward(state_)
target_actions = target_actions + \
T.clamp(T.tensor(np.random.normal(scale=0.2)), -0.5, 0.5)
target_actions = T.clamp(target_actions, self.min_action[0],
self.max_action[0])
q1_ = self.target_critic_1.forward(state_, target_actions)
q2_ = self.target_critic_2.forward(state_, target_actions)
q1 = self.critic_1.forward(state, action)
q2 = self.critic_2.forward(state, action)
q1_[done] = 0.0
q2_[done] = 0.0
q1_ = q1_.view(-1)
q2_ = q2_.view(-1)
critic_value_ = T.min(q1_, q2_)
target = reward + self.gamma * critic_value_
target = target.view(self.batch_size, 1)
self.critic_1.optimizer.zero_grad()
self.critic_2.optimizer.zero_grad()
q1_loss = F.mse_loss(target, q1)
q2_loss = F.mse_loss(target, q2)
critic_loss = q1_loss + q2_loss
critic_loss.backward()
self.critic_1.optimizer.step()
self.critic_2.optimizer.step()
self.learn_step_cntr += 1
if self.learn_step_cntr % self.update_actor_iter != 0:
return
self.actor.optimizer.zero_grad()
actor_q1_loss = self.critic_1.forward(state, self.actor.forward(state))
actor_loss = -T.mean(actor_q1_loss)
actor_loss.backward()
self.actor.optimizer.step()
self.update_network_parameters()
def update_network_parameters(self, tau=None):
if tau is None:
tau = self.tau
actor_params = self.actor.named_parameters()
critic_1_params = self.critic_1.named_parameters()
critic_2_params = self.critic_2.named_parameters()
target_actor_params = self.target_actor.named_parameters()
target_critic_1_params = self.target_critic_1.named_parameters()
target_critic_2_params = self.target_critic_2.named_parameters()
critic_1 = dict(critic_1_params)
critic_2 = dict(critic_2_params)
actor = dict(actor_params)
target_actor = dict(target_actor_params)
target_critic_1 = dict(target_critic_1_params)
target_critic_2 = dict(target_critic_2_params)
for name in critic_1:
critic_1[name] = tau*critic_1[name].clone() + \
(1-tau)*target_critic_1[name].clone()
for name in critic_2:
critic_2[name] = tau*critic_2[name].clone() + \
(1-tau)*target_critic_2[name].clone()
for name in actor:
actor[name] = tau*actor[name].clone() + \
(1-tau)*target_actor[name].clone()
self.target_critic_1.load_state_dict(critic_1)
self.target_critic_2.load_state_dict(critic_2)
self.target_actor.load_state_dict(actor)
def save_models(self):
self.actor.save_checkpoint()
self.target_actor.save_checkpoint()
self.critic_1.save_checkpoint()
self.critic_2.save_checkpoint()
self.target_critic_1.save_checkpoint()
self.target_critic_2.save_checkpoint()
def load_models(self):
self.actor.load_checkpoint()
self.target_actor.load_checkpoint()
self.critic_1.load_checkpoint()
self.critic_2.load_checkpoint()
self.target_critic_1.load_checkpoint()
self.target_critic_2.load_checkpoint()
class Agent():
def __init__(self, input_dims, env, n_actions):
self.memory = ReplayBuffer(input_dims)
self.n_actions = n_actions
self.actor_nn = ActorNetwork(input_dims,
n_actions=n_actions,
name=Constants.env_id + '_actor',
max_action=env.action_space.n)
self.critic_local_1_nn = CriticNetwork(input_dims,
n_actions=n_actions,
name=Constants.env_id +
'_critic_local_1')
self.critic_local_2_nn = CriticNetwork(input_dims,
n_actions=n_actions,
name=Constants.env_id +
'_critic_local_2')
self.critic_target_1_nn = CriticNetwork(input_dims,
n_actions=n_actions,
name=Constants.env_id +
'_critic_target_1')
self.critic_target_2_nn = CriticNetwork(input_dims,
n_actions=n_actions,
name=Constants.env_id +
'_critic_target_2')
self.value_nn = ValueNetwork(input_dims,
name=Constants.env_id + '_value')
self.target_value_nn = ValueNetwork(input_dims,
name=Constants.env_id +
'_target_value')
self.update_network_parameters(tau=1)
def choose_action(self, observation):
state = T.Tensor([observation]).to(Constants.device)
_, max_probability_action = self.actor_nn.sample_action(state)
return max_probability_action
def remember(self, state, action, reward, new_state, done):
self.memory.store_transition(state, action, reward, new_state, done)
def learn(self):
if self.memory.mem_cntr < Hyper.batch_size:
return
state, action, reward, next_state, done = self.memory.sample_buffer()
reward = T.tensor(reward, dtype=T.float).to(Constants.device)
done = T.tensor(done).to(Constants.device)
next_state = T.tensor(next_state, dtype=T.float).to(Constants.device)
state = T.tensor(state, dtype=T.float).to(Constants.device)
action = T.tensor(action, dtype=T.float).to(Constants.device)
# value_from_nn = self.value_nn(state).view(-1)
value_from_nn = self.value_nn(state)
new_value_from_nn = self.target_value_nn(next_state).view(-1)
new_value_from_nn[done] = 0.0
(action_probabilities,
log_action_probabilities), _ = self.actor_nn.sample_action(next_state)
with T.no_grad():
q1_new_policy = self.critic_target_1_nn(next_state)
q2_new_policy = self.critic_target_2_nn(next_state)
critic_value = T.min(q1_new_policy, q2_new_policy)
self.value_nn.optimizer.zero_grad()
# CHANGE0003 Soft state-value where actions are discrete
inside_term = Hyper.alpha * log_action_probabilities - critic_value
#value_target = action_probabilities * (critic_value - Hyper.alpha * log_action_probabilities)
value_loss = (action_probabilities * inside_term).sum(dim=1).mean()
value_loss.backward(retain_graph=True)
self.value_nn.optimizer.step()
(action_probabilities,
log_action_probabilities), _ = self.actor_nn.sample_action(state)
with T.no_grad():
q1_new_policy = self.critic_local_1_nn(state)
q2_new_policy = self.critic_local_1_nn(state)
critic_value = T.min(q1_new_policy, q2_new_policy)
# CHANGE0005 Objective for policy
actor_loss = action_probabilities * (
Hyper.alpha * log_action_probabilities - critic_value)
actor_loss = T.mean(actor_loss)
self.actor_nn.optimizer.zero_grad()
actor_loss.backward(retain_graph=True)
self.actor_nn.optimizer.step()
self.critic_local_1_nn.optimizer.zero_grad()
self.critic_local_2_nn.optimizer.zero_grad()
q_hat = Hyper.reward_scale * reward + Hyper.gamma * new_value_from_nn
action_logits1 = self.critic_local_1_nn(state)
q1_old_policy = T.argmax(action_logits1, dim=1, keepdim=True).view(-1)
action_logits2 = self.critic_local_2_nn(state)
q2_old_policy = T.argmax(action_logits2, dim=1, keepdim=True).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_local_1_nn.optimizer.step()
self.critic_local_2_nn.optimizer.step()
self.update_network_parameters()
def update_network_parameters(self, tau=None):
if tau is None:
tau = Hyper.tau
target_value_params = self.target_value_nn.named_parameters()
value_params = self.value_nn.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_nn.load_state_dict(value_state_dict)
self.update_network_parameters_line(
self.critic_target_1_nn.named_parameters(),
self.critic_local_1_nn.named_parameters(), tau)
self.update_network_parameters_line(
self.critic_target_2_nn.named_parameters(),
self.critic_local_2_nn.named_parameters(), tau)
def update_network_parameters_line(self, target_params, local_params, tau):
for target_param, local_param in zip(target_params, local_params):
target_param[1].data.copy_(tau * local_param[1].data +
(1.0 - tau) * target_param[1].data)
def save_models(self):
print('.... saving models ....')
self.actor_nn.save_checkpoint()
self.value_nn.save_checkpoint()
self.target_value_nn.save_checkpoint()
self.critic_local_1_nn.save_checkpoint()
self.critic_local_2_nn.save_checkpoint()
self.critic_target_1_nn.save_checkpoint()
self.critic_target_2_nn.save_checkpoint()
def load_models(self):
print('.... loading models ....')
self.actor_nn.load_checkpoint()
self.value_nn.load_checkpoint()
self.target_value_nn.load_checkpoint()
self.critic_local_1_nn.load_checkpoint()
self.critic_local_2_nn.load_checkpoint()
self.critic_target_1_nn.load_checkpoint()
self.critic_target_2_nn.load_checkpoint()
class Agent():
def __init__(self, alpha=0.0003, beta= 0.0003, input_dims=[8], env=None,
gamma=0.99, n_actions=2, max_size=1000000, tau=0.005,
ent_alpha = 0.0001, batch_size=256, reward_scale=2,
layer1_size=256, layer2_size=256, chkpt_dir='tmp/sac'):
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.ent_alpha = ent_alpha
self.reward_scale = reward_scale
self.actor = ActorNetwork(alpha, input_dims, n_actions=n_actions,
fc1_dims=layer1_size, fc2_dims=layer2_size ,
name='actor', chkpt_dir=chkpt_dir)
self.critic_1 = CriticNetwork(beta, input_dims, n_actions=n_actions,
fc1_dims=layer1_size, fc2_dims=layer2_size ,name='critic_1',
chkpt_dir=chkpt_dir)
self.critic_2 = CriticNetwork(beta, input_dims, n_actions=n_actions,
fc1_dims=layer1_size, fc2_dims=layer2_size ,name='critic_2',
chkpt_dir=chkpt_dir)
self.target_critic_1 = CriticNetwork(beta, input_dims, n_actions=n_actions,
fc1_dims=layer1_size, fc2_dims=layer2_size ,name='target_critic_1',
chkpt_dir=chkpt_dir)
self.target_critic_2 = CriticNetwork(beta, input_dims, n_actions=n_actions,
fc1_dims=layer1_size, fc2_dims=layer2_size ,name='target_critic_2',
chkpt_dir=chkpt_dir)
self.update_network_parameters(tau=1)
def choose_actions(self, observation, learn_mode=False):
if not learn_mode:
state = T.Tensor([observation]).to(self.actor.device)
else:
state = observation
action_probs = self.actor.forward(state)
max_probability_action = T.argmax(action_probs, dim=-1)
action_distribution = Categorical(action_probs)
action = action_distribution.sample().cpu().detach().numpy()[0]
z = action_probs == 0.0
z = z.float()*1e-8
log_probs = T.log(action_probs + z)
return action, action_probs, log_probs, max_probability_action
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_critic_1_params = self.target_critic_1.named_parameters()
target_critic_2_params = self.target_critic_2.named_parameters()
critic_1_params = self.critic_1.named_parameters()
critic_2_params = self.critic_2.named_parameters()
target_critic_1_state_dict = dict(target_critic_1_params)
critic_1_state_dict = dict(critic_1_params)
target_critic_2_state_dict = dict(target_critic_2_params)
critic_2_state_dict = dict(critic_2_params)
for name in critic_1_state_dict:
critic_1_state_dict[name] = tau*critic_1_state_dict[name].clone() + (1-tau)*target_critic_1_state_dict[name].clone()
for name in critic_2_state_dict:
critic_2_state_dict[name] = tau*critic_2_state_dict[name].clone() + (1-tau)*target_critic_2_state_dict[name].clone()
self.target_critic_1.load_state_dict(critic_1_state_dict)
self.target_critic_2.load_state_dict(critic_2_state_dict)
def save_models(self):
# print('.....saving models.....')
self.actor.save_checkpoint()
self.critic_1.save_checkpoint()
self.critic_2.save_checkpoint()
self.target_critic_1.save_checkpoint()
self.target_critic_2.save_checkpoint()
def load_models(self):
print('.....loading models.....')
self.actor.load_checkpoint()
self.critic_1.load_checkpoint()
self.critic_2.load_checkpoint()
self.target_critic_1.load_checkpoint()
self.target_critic_2.load_checkpoint()
def learn(self):
if self.memory.mem_cntr < self.batch_size:
return 10, 10, 10, 10, 10
state, action, reward, next_state, done = self.memory.sample_buffer(self.batch_size)
reward = T.tensor(reward, dtype=T.float).to(self.actor.device)
done = T.tensor(done, dtype=T.float).to(self.actor.device)
state_ = T.tensor(next_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)
# Critics Learning
with T.no_grad():
action_, probs, log_probs, max_action = self.choose_actions(state_, learn_mode=True)
qf1_target_ = self.target_critic_1(state_)
qf2_target_ = self.target_critic_2(state_)
min_qf_target_ = probs*(T.min(qf1_target_, qf2_target_) - self.ent_alpha*log_probs)
min_qf_target_ = min_qf_target_.sum(dim=1).view(-1)
next_q_value = reward + (1.0-done)*self.gamma*min_qf_target_
action = action.view(self.batch_size, 1)
qf1 = self.critic_1(state).gather(1, action.long())
qf2 = self.critic_2(state).gather(1, action.long())
self.critic_1.optimizer.zero_grad()
qf1_loss = F.mse_loss(qf1, next_q_value)
qf1_loss.backward(retain_graph=False)
self.critic_1.optimizer.step()
self.critic_2.optimizer.zero_grad()
qf2_loss = F.mse_loss(qf2, next_q_value)
qf2_loss.backward(retain_graph=False)
self.critic_2.optimizer.step()
self.update_network_parameters()
# Actor loss
qf1_pi = self.critic_1(state)
qf2_pi = self.critic_2(state)
min_qf_pi = T.min(qf1_pi, qf2_pi)
inside_term = self.ent_alpha*log_probs - min_qf_pi
actor_loss = (probs*inside_term).sum(dim=1).mean()
self.actor.optimizer.zero_grad()
actor_loss.backward(retain_graph=False)
self.actor.optimizer.step()
def compute_grads(self):
if self.memory.mem_cntr < self.batch_size:
return False
state, action, reward, next_state, done = self.memory.sample_buffer(self.batch_size)
reward = T.tensor(reward, dtype=T.float).to(self.actor.device)
done = T.tensor(done, dtype=T.float).to(self.actor.device)
state_ = T.tensor(next_state, dtype=T.float).to(self.actor.device)
action = T.tensor(action, dtype=T.float).to(self.actor.device)
state = T.tensor(state, dtype=T.float).to(self.actor.device)
state.requires_grad = True
with T.no_grad():
action_, probs, log_probs, max_action = self.choose_actions(state_, learn_mode=True)
qf1_target_ = self.target_critic_1(state_)
qf2_target_ = self.target_critic_2(state_)
min_qf_target_ = probs*(T.min(qf1_target_, qf2_target_) - self.ent_alpha*log_probs)
min_qf_target_ = min_qf_target_.sum(dim=1).view(-1)
next_q_value = reward + (1.0-done)*self.gamma*min_qf_target_
self.actor.optimizer.zero_grad()
qf1_pi = self.critic_1(state)
qf2_pi = self.critic_2(state)
min_qf_pi = T.min(qf1_pi, qf2_pi)
inside_term = self.ent_alpha*log_probs - min_qf_pi
actor_loss = (probs*inside_term).sum(dim=1).mean()
actor_loss.backward()
data_grad = state.grad.data
return data_grad.mean(axis=0)
