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()
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_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():
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():
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')
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()