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)
q_env = []
q_network = []
#print(states)
observation = env.reset(init_x=np.array([100]), max_steps=2000)
for x in states:
q_env.append([])
q_network.append([])
for u in actions:
# states.append(x)
# actions.append(u)
q_env[-1].append(np.squeeze(env.get_Q(x, u)))
#q_env.append(np.squeeze(env.get_Q(x, u)))
q1_new_policy = np.squeeze(
critic_1.forward(T.tensor(x), T.tensor(u)).detach().numpy())
q2_new_policy = np.squeeze(
critic_2.forward(T.tensor(x), T.tensor(u)).detach().numpy())
#print(q1_new_policy)
q = np.minimum(q1_new_policy, q2_new_policy)
#q_network.append(q)
q_network[-1].append(q)
q_env = q_env / np.max(np.abs(q_env))
q_network = q_network / np.max(np.abs(q_network))
states = np.squeeze(states)
actions = np.squeeze(actions)
q_env = np.squeeze(q_env)
q_network = np.squeeze(q_network)
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(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,
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:
""" This class represents the reinforcement learning agent """
def __init__(self,
state_size: int,
action_size: int,
gamma: float = 0.99,
lr_actor: float = 0.001,
lr_critic: float = 0.003,
weight_decay: float = 0.0001,
tau: float = 0.001,
buffer_size: int = 100000,
batch_size: int = 64):
"""
:param state_size: how many states does the agent get as input (input size of neural networks)
:param action_size: from how many actions can the agent choose
:param gamma: discount factor
:param lr_actor: learning rate of the actor network
:param lr_critic: learning rate of the critic network
:param weight_decay:
:param tau: soft update parameter
:param buffer_size: size of replay buffer
:param batch_size: size of learning batch (mini-batch)
"""
self.tau = tau
self.gamma = gamma
self.batch_size = batch_size
self.actor_local = ActorNetwork(state_size, action_size).to(device)
self.actor_target = ActorNetwork(state_size, action_size).to(device)
self.actor_optimizer = optim.Adam(self.actor_local.parameters(),
lr=lr_actor)
print(self.actor_local)
self.critic_local = CriticNetwork(state_size, action_size).to(device)
self.critic_target = CriticNetwork(state_size, action_size).to(device)
self.critic_optimizer = optim.Adam(self.critic_local.parameters(),
lr=lr_critic,
weight_decay=weight_decay)
print(self.critic_local)
self.hard_update(self.actor_local, self.actor_target)
self.hard_update(self.critic_local, self.critic_target)
self.memory = ReplayBuffer(action_size, buffer_size, batch_size)
# this would probably also work with Gaussian noise instead of Ornstein-Uhlenbeck process
self.noise = OUNoise(action_size)
def step(self, experience: tuple):
"""
:param experience: tuple consisting of (state, action, reward, next_state, done)
:return:
"""
self.memory.add(*experience)
if len(self.memory) > self.batch_size:
experiences = self.memory.sample()
self.learn(experiences)
def act(self, state, add_noise: bool = True):
""" Actor uses the policy to act given a state """
state = torch.from_numpy(state).float().to(device)
self.actor_local.eval()
with torch.no_grad():
action = self.actor_local.forward(state).cpu().data.numpy()
self.actor_local.train()
if add_noise:
action += self.noise.sample()
return np.clip(action, -1, 1)
def learn(self, experiences):
# Q_targets = r + γ * critic_target(next_state, actor_target(next_state))
# the actor_target returns the next action, this next action is then used (with the state) to estimate
# the Q-value with the critic_target network
states, actions, rewards, next_states, dones = experiences
# region Update Critic
actions_next = self.actor_target.forward(next_states)
q_expected = self.critic_local.forward(states, actions)
q_targets_next = self.critic_target.forward(next_states, actions_next)
q_targets = rewards + (self.gamma * q_targets_next * (1 - dones))
# minimize the loss
critic_loss = F.mse_loss(q_expected, q_targets)
self.critic_optimizer.zero_grad()
critic_loss.backward()
torch.nn.utils.clip_grad_norm_(self.critic_local.parameters(), 1)
self.critic_optimizer.step()
# endregion Update Critic
# region Update actor
# Compute actor loss
actions_predictions = self.actor_local.forward(states)
actor_loss = -self.critic_local.forward(states,
actions_predictions).mean()
# Minimize actor loss
self.actor_optimizer.zero_grad()
actor_loss.backward()
self.actor_optimizer.step()
# endregion Update actor
# region update target network
self.soft_update(self.critic_local, self.critic_target)
self.soft_update(self.actor_local, self.actor_target)
# endregion update target network
def soft_update(self, local_model, target_model):
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 hard_update(self, local_model, target_model):
"""Copy the weights and biases from the local to the target network"""
for target_param, param in zip(target_model.parameters(),
local_model.parameters()):
target_param.data.copy_(param.data)
def reset(self):
self.noise.reset()
