class AgentDDPG:
def __init__(self, state_size, action_size, seed):
"""
:state_size: size of the state vector
:action_size: size of the action vector
"""
self.state_size = state_size
self.action_size = action_size
self.t_step = 0
self.score = 0.0
self.best = 0.0
self.seed = seed
self.learning_rate_actor = 0.0001
self.learning_rate_critic = 0.001
# Instances of the policy function or actor and the value function or critic
# Actor critic with Advantage
# Actor local and target network definitions
self.actor_local = Actor(self.state_size, self.action_size,
self.seed).to(device)
self.actor_target = Actor(self.state_size, self.action_size,
self.seed).to(device)
# Critic local and target
self.critic_local = Critic(self.state_size, self.action_size,
self.seed).to(device)
self.critic_target = Critic(self.state_size, self.action_size,
self.seed).to(device)
# Actor Optimizer
self.actor_optimizer = optim.Adam(self.actor_local.parameters(),
lr=self.learning_rate_actor)
# Critic Optimizer
self.critic_optimizer = optim.Adam(self.critic_local.parameters(),
lr=self.learning_rate_critic)
# Make sure local and target start with the same weights
self.actor_target.load_state_dict(self.actor_local.state_dict())
self.critic_target.load_state_dict(self.critic_local.state_dict())
# Parameters for the Algorithm
self.gamma = 0.99 # Discount factor
self.tau = 0.001 # Soft update for target parameters Actor Critic with Advantage
# Actor determines what to do based on the policy
def act_local(self, state):
# Given a state return the action recommended by the policy actor_local
# Reshape the state to fit the torch tensor input
state = torch.from_numpy(state).float().unsqueeze(0).to(device)
# Pass the state to the actor local model to get an action
# recommend for the policy in a state
# set the actor_local model to predict not to train
self.actor_local.eval()
# set the model so this operation is not counted in the
# gradiant calculation.
with torch.no_grad():
actions = self.actor_local(state)
# set the model back to training mode
self.actor_local.train()
# Return actions tensor
return actions.detach()
def act_target(self, states):
# Pass the state to the actor target model to get an action
# recommend for the policy in a state
# set the actor_target model to predict not to train
self.actor_target.eval()
# set the model so this operation is not counted in the
# gradiant calculation.
with torch.no_grad():
actions = self.actor_target(states)
# set the model back to training mode
self.actor_target.train()
# Return actions tensor
return actions.detach()
def get_episode_score(self):
"""
Calculate the episode scores
:return: None
"""
# Update score and best score
self.score = self.total_reward / float(
self.count) if self.count else 0.0
if self.score > self.best:
self.best = self.score
def save_model_weights(self):
torch.save(self.actor_local.state_dict(), './checkpoints.pkl')
class AgentDDPG:
def __init__(self, state_size, action_size, seed):
"""
:state_size: size of the state vector
:action_size: size of the action vector
"""
self.state_size = state_size
self.action_size = action_size
self.t_step = 0
self.score = 0.0
self.best = 0.0
self.seed = seed
self.total_reward = 0.0
self.count = 0
self.learning_rate_actor = 0.0001
self.learning_rate_critic = 0.001
self.batch_size = 128
self.update_every = 1
# Instances of the policy function or actor and the value function or critic
# Actor critic with Advantage
# Actor local and target network definitions
self.actor_local = Actor(self.state_size, self.action_size,
self.seed).to(device)
self.actor_target = Actor(self.state_size, self.action_size,
self.seed).to(device)
# Critic local and target
self.critic_local = Critic(self.state_size, self.action_size,
self.seed).to(device)
self.critic_target = Critic(self.state_size, self.action_size,
self.seed).to(device)
# Actor Optimizer
self.actor_optimizer = optim.Adam(self.actor_local.parameters(),
lr=self.learning_rate_actor)
# Critic Optimizer
self.critic_optimizer = optim.Adam(self.critic_local.parameters(),
lr=self.learning_rate_critic)
# Make sure local and target start with the same weights
self.actor_target.load_state_dict(self.actor_local.state_dict())
self.critic_target.load_state_dict(self.critic_local.state_dict())
# Initialize the Gaussin Noise process
self.exploration_mu = 0
self.exploration_theta = 0.15
self.exploration_sigma = 0.2
self.noise = OUNoise(self.action_size, self.exploration_mu,
self.exploration_theta, self.exploration_sigma)
# Initialize the Replay Memory
self.buffer_size = 1000000
self.memory = ReplayBuffer(self.buffer_size, self.batch_size)
# Parameters for the Algorithm
self.gamma = 0.99 # Discount factor
self.tau = 0.001 # Soft update for target parameters Actor Critic with Advantage
# Actor interact with the environment through the step
def step(self, state, action, reward, next_state, done):
# Add to the total reward the reward of this time step
self.total_reward += reward
# Increase your count based on the number of rewards
# received in the episode
self.count += 1
# Stored experience tuple in the replay buffer
self.memory.add(state, action, reward, next_state, done)
# Learn every update_times time steps.
self.t_step = (self.t_step + 1) % self.update_every
if self.t_step == 0:
# Check to see if you have enough to produce a batch
# and learn from it
if len(self.memory) > self.batch_size:
experiences = self.memory.sample()
# Train the networks using the experiences
self.learn(experiences)
# Roll over last state action (not needed)
# self.last_state = next_state
# Actor determines what to do based on the policy
def act(self, state):
# Given a state return the action recommended by the policy
# Reshape the state to fit the torch tensor input
state = torch.from_numpy(state).float().unsqueeze(0).to(device)
# Pass the state to the actor local model to get an action
# recommend for the policy in a state
# set the actor_local model to predict not to train
self.actor_local.eval()
# set the model so this operation is not counted in the
# gradiant calculation.
with torch.no_grad():
actions = self.actor_local(state)
# set the model back to training mode
self.actor_local.train()
# Because we are exploring we add some noise to the
# action vector
return list(actions.detach().numpy().reshape(4, ) +
self.noise.sample())
# This is the Actor learning logic called when the agent
# take a step to learn
def learn(self, experiences):
"""
Learning means that the networks parameters needs to be updated
Using the experineces batch.
Network learns from experiences not form interaction with the
environment
"""
# Reshape the experience tuples in separate arrays of states, actions
# rewards, next_state, done
# Your are converting every member of the tuple in a column or vector
states = np.vstack([e.state for e in experiences if e is not None])
actions = np.array([e.action for e in experiences
if e is not None]).astype(np.float32).reshape(
-1, self.action_size)
rewards = np.array([e.reward for e in experiences if e is not None
]).astype(np.float32).reshape(-1, 1)
dones = np.array([e.done for e in experiences
if e is not None]).astype(np.uint8).reshape(-1, 1)
next_states = np.vstack(
[e.next_state for e in experiences if e is not None])
# Now reshape the numpy arrays for states, actions and next_states to torch tensors
# rewards and dones does not need to be tensors.
states = torch.from_numpy(states).float().unsqueeze(0).to(device)
actions = torch.from_numpy(actions).float().unsqueeze(0).to(device)
next_states = torch.from_numpy(next_states).float().unsqueeze(0).to(
device)
# Firs we pass a batch of next states to the actor so it tell us what actions
# to execute, we use the actor target network instead of the actor local network
# because of the advantage principle
# set the target network to predict because this is not part of the training, this model
# weights are alter by a soft update not by an optimizer
self.actor_target.eval()
with torch.no_grad():
next_state_actions = self.actor_target(next_states).detach()
self.actor_target.train()
# The critic evaluates the actions taking by the actor in the next state and generates the
# Q(a,s) value of the next state taking those actions. These action, next_state tuple comes from the
# ReplayBuffer not from interacting with the environment.
# Remember the Critic or q_value function inputs is states, actions
# We calculate the q_targets of the next state. We will use this to calculate the current
# state q_value using the bellman equation.
# set the target network to predict because this is not part of the training, this model
# weights are alter by a soft update not by an optimizer
self.critic_target.eval()
with torch.no_grad():
q_targets_next_state_action_values = self.critic_target(
next_states, next_state_actions).detach()
self.actor_target.train()
# With the next state q_value that is a vector of action values Q(s,a) of a random selected
# next_states from the replay buffer. We calculate the CURRENT state target Q(s,a).
# using the TD one-step Sarsa equations and the q_target_next value we got from the critic_target net
# We make terminal states target Q(s,a) 0 and Non terminal the Q_targtes value
# This is done to train the critic_local model in a supervise learning fashion, this is the target values.
q_targets = torch.from_numpy(
rewards + self.gamma * q_targets_next_state_action_values.numpy() *
(1 - dones)).float()
# --- Optimize the local Critic Model ----#
# Here we start the supervise training process of the critic_local network
# we pass a bunch of states actions samples it produces the expected output
# q_value of each action we passed.
q_expected = self.critic_local(states, actions)
# Clear grad buffer values in preparation.
self.critic_optimizer.zero_grad()
# loss function for the critic_local model mean square of the difference
# between the q_expected value and the q_target value.
critic_loss = F.smooth_l1_loss(q_expected, q_targets)
critic_loss.backward(retain_graph=True)
# gradient clipping
torch.nn.utils.clip_grad_norm_(self.critic_local.parameters(), 1)
# optimize the critic_local model using the optimizer defined for the critic
# In the init function of this class
self.critic_optimizer.step()
# --- Optimize the local Actor Model ---#
# Get the actor actions using the experience buffer states
actor_actions = self.actor_local(states)
# Use as a loss the negative sum of the q_values produce by the optimized critic local model given the
# action of the actor_local model obtain using the states of the sampled buffer.
loss_actor = -1 * torch.sum(
self.critic_local.forward(states, actor_actions))
# Set the model gradients to zero in preparation
self.actor_optimizer.zero_grad()
# Back propagate
loss_actor.backward()
# optimize the actor_local model using the optimizer defined for the actor
# In the init function of this class
self.actor_optimizer.step()
# Soft-update target models
self.soft_update(self.critic_local, self.critic_target)
self.soft_update(self.actor_local, self.actor_target)
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 get_episode_score(self):
"""
Calculate the episode scores
:return: None
"""
# Update score and best score
self.score = self.total_reward / float(
self.count) if self.count else 0.0
if self.score > self.best:
self.best = self.score
def save_model_weights(self):
torch.save(self.actor_local.state_dict(), './checkpoints.pkl')
class TD3(object):
# Twin Delay DDGP
def __init__(self, state_dim, action_dim, max_action):
self.actor = Actor(state_dim, action_dim, max_action).to(device)
self.actor_target = Actor(state_dim, action_dim, max_action).to(device)
self.actor_target.load_state_dict(self.actor.state_dict())
self.actor_optimizer = torch.optim.Adam(self.actor.parameters())
self.critic = Critic(state_dim, action_dim).to(device)
self.critic_target = Critic(state_dim, action_dim).to(device)
self.critic_target.load_state_dict(self.critic.state_dict())
self.critic_optimizer = torch.optim.Adam(self.critic.parameters())
self.max_action = max_action
def select_action(self, state):
state = torch.Tensor(state.reshape(1, -1)).to(device)
return self.actor(state).cpu().data.numpy().flatten()
def train(self,
replay_buffer,
iterations,
batch_size=100,
discount=0.99,
tau=0.005,
policy_noise=0.2,
noise_clip=0.5,
policy_freq=2):
for it in range(iterations):
# step 4: we sample batch of transitions (s, s', a, r) from the memory
batch_state, batch_next_state, batch_actions, batch_rewards, batch_dones = replay_buffer.sample(
batch_size)
state = torch.Tensor(batch_state).to(device)
next_state = torch.Tensor(batch_next_state).to(device)
action = torch.Tensor(batch_actions).to(device)
reward = torch.Tensor(batch_rewards).to(device)
done = torch.Tensor(batch_dones).to(device)
# step 5: from the next state s', the actor traget plays next action a'
next_action = self.actor_target(next_state)
# step 6: we add Gaussian noise to the next action a' and we clamp it in a range of values supported by the environment
noise = torch.Tensor(batch_actions).data.normal_(
0, policy_noise).to(device)
noise = noise.clamp(-noise_clip, noise_clip)
next_action = (next_action + noise).clamp(-self.max_action,
self.max_action)
# step 7: the two critic targets take each the couple (s', a') as input and return 2 Q-values Qt1(s', a') and Qt2(s', a') as output
target_Q1, target_Q2 = self.critic_target(next_state, next_action)
# step 8: we keep the minimum of these Q-values min(Qt1, Qt2)
target_Q = torch.min(target_Q1, target_Q2)
# step 9: we get the final target of the 2 critic models, which is : Qt = r + gamma * target_Q
target_Q = reward + (1 - done) * discount * target_Q
# step 10: the 2 critic models take each the couple (s, a) as input and return 2 Q-values Qt1(s, a) and Qt2(s, a) as outputs
current_Q1, current_Q2 = self.critic(state, action)
# step 11: we compute the less coming from the 2 critic models : critic loss = mse_loss(Q1(s,a), Qt) + mse_loss(Q2(s,a), Qt)
critic_loss = F.mse_loss(current_Q1, target_Q) + F.mse_loss(
current_Q2, target_Q)
# step 12: we backpropagate the critic loss and update the parameters of the 2 critic models with SGD optimizer
self.critic_optimizer.zero_grad()
critic_loss.backward()
self.critic_optimizer.step()
# step 13: one every 2 iterations, we update our Actor model by performing gradient ascent on the output of the first critic model
if it % policy_freq == 0:
# deterministic policy gradient DPG
actor_loss = -self.critic.Q1(state, self.actor(state)).mean()
self.actor.zero_grad()
actor_loss.backward()
self.actor_optimizer.step()
# Delay
# step 14: still once every 2 iterations, we update the weights of the actor target by polyak averaging
for param, target_param in zip(self.actor.parameters(),
self.actor_target.parameters()):
target_param.data.copy_(tau * param.data +
(1 - tau) * target_param.data)
# step 15: still ones every 2 iterations, we uodate the weights of the critic target by polyak averaging
for param, target_param in zip(
self.critic.parameters(),
self.critic_target.parameters()):
target_param.data.copy_(tau * param.data +
(1 - tau) * target_param.data)
def save(self, filename, directory):
torch.save(self.actor.state_dict(),
'%s/%s_actor.pth' % (directory, filename))
torch.save(self.critic.state_dict(),
'%s/%s_critic.pth' % (directory, filename))
def load(self, filename, directory):
self.actor.load_state_dict(
torch.load('%s/%s_actor.pth' % (directory, filename)))
self.critic.load_state_dict(
torch.load('%s/%s_critic.pth' % (directory, filename)))
class Agent:
def __init__(self, device, state_size, action_size, buffer_size=10,
batch_size=10,
actor_learning_rate=1e-4,
critic_learning_rate=1e-3,
discount_rate=0.99,
tau=0.1,
steps_per_update=4,
action_range=None,
dropout_p=0.0,
weight_decay=0.0001,
noise_max=0.2,
noise_decay=1.0,
n_agents=1
):
self.device: torch.device = device
self.state_size = state_size
self.action_size = action_size
self.critic_control = Critic(state_size, action_size).to(device)
self.critic_control.dropout.p = dropout_p
self.critic_target = Critic(state_size, action_size).to(device)
self.critic_target.eval()
self.critic_optimizer = torch.optim.Adam(
self.critic_control.parameters(),
weight_decay=weight_decay,
lr=critic_learning_rate)
self.actor_control = Actor(state_size, action_size, action_range).to(
device)
self.actor_control.dropout.p = dropout_p
self.actor_target = Actor(state_size, action_size, action_range).to(
device)
self.actor_target.eval()
self.actor_optimizer = torch.optim.Adam(
self.actor_control.parameters(),
weight_decay=weight_decay,
lr=actor_learning_rate)
self.batch_size = batch_size
self.min_buffer_size = batch_size
self.replay_buffer = ReplayBuffer(device, state_size, action_size,
buffer_size)
self.discount_rate = discount_rate
self.tau = tau
self.step_count = 0
self.steps_per_update = steps_per_update
self.noise_max = noise_max
self.noise = OUNoise([n_agents, action_size], 15071988, sigma=self.noise_max)
self.noise_decay = noise_decay
self.last_score = float('-inf')
def policy(self, state, add_noise=True):
state = torch.from_numpy(state).float().to(self.device)
self.actor_control.eval()
with torch.no_grad():
action = self.actor_control(state).cpu().numpy()
self.actor_control.train()
if add_noise:
noise = self.noise.sample()
action += noise
return action
def step(self, state, action, reward, next_state, done):
p = self.calculate_p(state, action, reward, next_state, done)
for i in range(state.shape[0]):
self.replay_buffer.add(state[i, :], action[i, :], reward[i],
next_state[i, :], done[i], p[i])
if self.step_count % self.steps_per_update == 0:
self.learn()
self.step_count += 1
def learn(self):
if len(self.replay_buffer) < self.min_buffer_size:
return
indicies, (states, actions, rewards, next_states, dones, p) = \
self.replay_buffer.sample(self.batch_size)
self.actor_control.eval()
error = self.bellman_eqn_error(
states, actions, rewards, next_states, dones)
self.actor_control.train()
importance_scaling = (self.replay_buffer.buffer_size * p) ** -1
importance_scaling /= importance_scaling.max()
self.critic_optimizer.zero_grad()
loss = (importance_scaling * (error ** 2)).sum() / self.batch_size
loss.backward()
self.critic_optimizer.step()
self.actor_optimizer.zero_grad()
expected_actions = self.actor_control(states)
critic_score = self.critic_control(states, expected_actions)
loss = -1 * (importance_scaling * critic_score).sum() / self.batch_size
loss.backward()
self.actor_optimizer.step()
self.update_target(self.critic_control, self.critic_target)
self.update_target(self.actor_control, self.actor_target)
self.replay_buffer.update(indicies, error.detach().abs().cpu() + 1e-3)
def bellman_eqn_error(self, states, actions, rewards, next_states, dones):
"""Double DQN error - use the control network to get the best action
and apply the target network to it to get the target reward which is
used for the bellman eqn error.
"""
next_actions = self.actor_control(next_states)
target_action_values = self.critic_target(next_states, next_actions)
target_rewards = (
rewards
+ self.discount_rate * (1 - dones) * target_action_values
)
current_rewards = self.critic_control(states, actions)
error = current_rewards - target_rewards
return error
def calculate_p(self, state, action, reward, next_state, done):
next_state = torch.from_numpy(next_state).float().to(
self.device)
state = torch.from_numpy(state).float().to(self.device)
action = torch.from_numpy(action).float().to(self.device)
reward = torch.from_numpy(reward).float().to(self.device)
done = torch.from_numpy(done).float().to(
self.device)
done = done.unsqueeze(1)
reward = reward.unsqueeze(1)
self.actor_control.eval()
self.critic_control.eval()
with torch.no_grad():
retval = abs(
self.bellman_eqn_error(state, action, reward, next_state,
done)) + 1e-3
self.critic_control.train()
self.actor_control.train()
return retval
def update_target(self, control, target):
for target_param, control_param in zip(
target.parameters(),
control.parameters()):
target_param.data.copy_(
self.tau * control_param.data + (1.0 - self.tau) *
target_param.data)
def end_of_episode(self, final_score):
self.step_count = 0
self.noise.sigma *= self.noise_decay
self.last_score = final_score
self.noise.reset()
def save(self, path):
torch.save(self.critic_control.state_dict(), path + '-critic.p')
torch.save(self.actor_control.state_dict(), path + '-actor.p')
def restore(self, path):
self.critic_control.load_state_dict(
torch.load(path + '-critic.p', map_location='cpu'))
self.actor_control.load_state_dict(
torch.load(path + '-actor.p', map_location='cpu'))
class ActorCritic(Model):
def __init__(self,
observation_space_size,
action_space_size,
name=None,
env_name=None,
model_config=None,
play_mode=False):
if name is None:
name = "Unnamed-ActorCritic"
super(ActorCritic,
self).__init__(observation_space_size, action_space_size, name,
env_name, model_config, play_mode)
def build_model(self):
self.policy_net = Actor(self.observation_space_size,
self.action_space_size)
self.critic_net = Critic(self.observation_space_size)
if self.model_config is None:
self.gamma = 0.99
self.actor_optimizer = optim.Adam(self.policy_net.parameters())
self.actor_loss = nn.MSELoss()
self.critic_optimizer = optim.Adam(self.critic_net.parameters())
self.critic_loss = nn.MSELoss()
self.get_epsilon = self.get_epsilon_default
else:
pass
def save_checkpoint(self, n=0, filepath=None):
"""
n - number of epoch / episode or whatever is used for enumeration
"""
# TO DO: ADD OTHER RELEVANT PARAMETERS
checkpoint = {
'policy': self.policy_net.state_dict(),
'critic': self.critic_net.state_dict(),
'optimizer': self.optimizer.state_dict()
}
super(ActorCritic, self).save_checkpoint(n, filepath, checkpoint)
def load_checkpoint(self, filepath):
# TO DO: ADD OTHER RELEVANT parameters
checkpoint = torch.load(filepath)
self.policy_net.load_state_dict(checkpoint['policy'])
self.critic_net.load_state_dict(checkpoint['critic'])
self.optimizer.load_state_dict(checkpoint['optimizer'])
def prepare_sample(self, sample):
sample = np.array(sample)
states = torch.tensor(sample[:, 0], dtype=torch.float32)
actions = torch.tensor(sample[:, 1], dtype=torch.float32)
rewards = torch.tensor(sample[:, 2], dtype=torch.float32)
next_states = torch.tensor(sample[:, 3], dtype=torch.float32)
dones = torch.tensor(sample[:, 4], dtype=torch.int32)
return states, actions, rewards, next_states, dones
def critic_update(self, V, V_target):
self.critic_optimizer.zero_grad()
critic_loss = self.critic_loss(V, V_target)
critic_loss.backward()
self.critic_optimizer.step()
return critic_loss.item()
def actor_update(self, advantages, actions, mus):
self.actor_optimizer.zero_grad()
actor_loss = self.actor_loss(actions, mus)
gradient_term = advantages * actor_loss
gradient_term.backward()
self.actor_optimizer.step()
return actor_loss.item()
def update(self, sample, prepare_state=None):
actor_running_loss = []
critic_running_loss = []
for state, action, reward, next_state, done in sample:
if prepare_state is not None:
state = prepare_state(state)
next_state = prepare_state(next_state)
state = torch.tensor(state, dtype=torch.float32)
next_state = torch.tensor(next_state, dtype=torch.float32)
action = torch.tensor(action, dtype=torch.float32)
# Update Critic
V = self.critic_net.forward(state)
V_target = torch.tensor([reward], dtype=torch.float32)
if done is False:
V_target += self.gamma * self.critic_net.forward(next_state)
critic_loss = self.critic_update(V, V_target)
critic_running_loss.append(critic_loss)
# Update Actor
advantage = (V_target - V).detach()
mu = self.policy_net(state)
actor_loss = self.actor_update(advantage, action, mu)
actor_running_loss.append(actor_loss)
return actor_running_loss, critic_running_loss
def batch_update(self, sample, prepare_state=None):
actor_running_loss = []
critic_running_loss = []
states, actions, rewards, next_states, dones = self.prepare_sample(
sample)
# Update Critic
V = self.critic_net.forward(states)
V_target = rewards + self.gamma * self.critic_net.forward(
next_states) * (1 - dones)
critic_loss = self.critic_update(V, V_target)
critic_running_loss.append(critic_loss)
# Update Actor
advantage = (V_target - V).detach()
mu = self.policy_net(states)
actor_loss = self.actor_update(advantage, actions, mu)
actor_running_loss.append(actor_loss)
return actor_running_loss, critic_running_loss
class Agent(object):
def __init__(self, n_states, n_actions, lr_actor, lr_critic, tau, gamma,
mem_size, actor_l1_size, actor_l2_size, critic_l1_size,
critic_l2_size, batch_size):
self.gamma = gamma
self.tau = tau
self.memory = ReplayBuffer(mem_size, n_states, n_actions)
self.batch_size = batch_size
self.actor = Actor(lr_actor, n_states, n_actions, actor_l1_size,
actor_l2_size)
self.critic = Critic(lr_critic, n_states, n_actions, critic_l1_size,
critic_l2_size)
self.target_actor = Actor(lr_actor, n_states, n_actions, actor_l1_size,
actor_l2_size)
self.target_critic = Critic(lr_critic, n_states, n_actions,
critic_l1_size, critic_l2_size)
self.noise = OUActionNoise(mu=np.zeros(n_actions), sigma=0.005)
self.update_network_parameters(tau=1)
def choose_action(self, observation):
self.actor.eval()
observation = torch.tensor(observation,
dtype=torch.float).to(self.actor.device)
mu = self.actor.forward(observation).to(self.actor.device)
# add noise to action - for exploration
mu_prime = mu + torch.tensor(self.noise(), dtype=torch.float).to(
self.actor.device)
self.actor.train()
return mu_prime.cpu().detach().numpy()
def choose_action_no_train(self, observation):
self.actor.eval()
observation = torch.tensor(observation,
dtype=torch.float).to(self.actor.device)
mu = self.actor.forward(observation).to(self.actor.device)
return mu.cpu().detach().numpy()
def remember(self, state, action, reward, new_state, done):
self.memory.push(state, action, reward, new_state, done)
def learn(self):
if self.memory.idx_last < self.batch_size:
# not enough data in replay buffer
return
# select random events
state, action, reward, new_state, done = self.memory.sample_buffer(
self.batch_size)
reward = torch.tensor(reward, dtype=torch.float).to(self.critic.device)
done = torch.tensor(done).to(self.critic.device)
new_state = torch.tensor(new_state,
dtype=torch.float).to(self.critic.device)
action = torch.tensor(action, dtype=torch.float).to(self.critic.device)
state = torch.tensor(state, dtype=torch.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 = torch.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 = torch.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)
def save_models(self):
timestamp = time.strftime("%Y%m%d-%H%M%S")
self.actor.save("actor_" + timestamp)
self.target_actor.save("target_actor_" + timestamp)
self.critic.save("critic_" + timestamp)
self.target_critic.save("target_critic_" + timestamp)
def load_models(self, fn_actor, fn_target_actor, fn_critic,
fn_target_critic):
self.actor.load_checkpoint(fn_actor)
self.target_actor.load_checkpoint(fn_target_actor)
self.critic.load_checkpoint(fn_critic)
self.target_critic.load_checkpoint(fn_target_critic)
class TD3(object):
"""Agent class that handles the training of the networks
and provides outputs as actions.
Args:
state_dim (array): state size
action_dim (array): action size
policy_noise (float): how much noise to add to actions
device (device): cuda or cpu to process the tensors
discount (float): discount factor
tau (float): soft update for main networks to target networks
policy_noise (float): noise factor
noise_clip (float): clip factor
policy_freq (int): frequency of policy updates
"""
def __init__(self, state_dim, action_dim, max_action, discount, tau,
policy_noise, noise_clip, policy_freq, device):
self.state_dim = len(state_dim[0])
self.action_dim = len(action_dim)
self.max_action = max_action[2]
self.actor = Actor(self.state_dim, self.action_dim,
self.max_action).to(device)
self.actor_target = copy.deepcopy(self.actor).float()
# self.actor_target = Actor(state_dim, action_dim, self.max_action).to(device)
# self.actor_target.load_state_dict(self.actor.state_dict())
self.actor_optimizer = torch.optim.Adam(self.actor.parameters(),
lr=3e-4) # or 1e-3
self.critic = Critic(self.state_dim, self.action_dim).to(device)
self.critic_target = copy.deepcopy(self.critic).float()
# self.critic_target = Critic(state_dim, action_dim).to(device)
# self.critic_target.load_state_dict(self.critic.state_dict())
self.critic_optimizer = torch.optim.Adam(self.critic.parameters(),
lr=3e-4) # or 1e-2
self.device = device
self.max_action = max_action
self.discount = discount
self.tau = tau
self.policy_noise = policy_noise
self.noise_clip = noise_clip
self.policy_freq = policy_freq
self.total_it = 0
def select_action(self, state):
"""Select an appropriate action from the agent policy
Args:
state (array): current state of environment
Returns:
action (float): action clipped within action range
"""
state = torch.FloatTensor(state.reshape(1, -1)).to(self.device)
# if noise != 0:
# action_dim = len(self.env.action_space())
# action = (action + np.random.normal(0, noise, size=action_dim))
# action_space_low, _, action_space_high = self.env.action_domain()
# return action.clip(action_space_low, action_space_high)
return self.actor(state).cpu().data.numpy().flatten()
def train(self, replay_buffer, batch_size=100):
"""Train and update actor and critic networks
Args:
replay_buffer (ReplayBuffer): buffer for experience replay
batch_size(int): batch size to sample from replay buffer
Return:
actor_loss (float): loss from actor network
critic_loss (float): loss from critic network
"""
self.total_it += 1
# Sample replay buffer
state, next_state, action, reward, done = replay_buffer.sample(
batch_size)
state = torch.from_numpy(
np.asarray([np.array(i.item().values()) for i in state]))
next_state = np.asarray(
[np.array(i.item().values()) for i in next_state])
reward = torch.as_tensor(reward, dtype=torch.float32)
done = torch.as_tensor(done, dtype=torch.float32)
with torch.no_grad():
# select an action according to the policy an add clipped noise
# need to select set of actions
noise = (torch.rand_like(torch.from_numpy(action)) *
self.policy_noise).clamp(-self.noise_clip,
self.noise_clip)
next_action = (self.actor_target(
torch.tensor(next_state, dtype=torch.float32)) +
torch.tensor(noise, dtype=torch.float32)).clamp(
self.max_action[0], self.max_action[2])
# next_action_d =torch.as_tensor(next_action, dtype=torch.double)
# Compute the target Q value
target_Q1, target_Q2 = self.critic(state, next_action)
target_Q = torch.min(target_Q1, target_Q2)
target_Q = reward + done * self.discount * target_Q
# update action datatype, can't do earlier, use np.array earlier
action = torch.as_tensor(action, dtype=torch.float32)
# get current Q estimates
current_Q1, current_Q2 = self.critic(state, action)
# compute critic loss
critic_loss = F.mse_loss(current_Q1, target_Q[:1, :].transpose(
0, 1)) + F.mse_loss(current_Q2, target_Q[:1, :].transpose(0, 1))
# optimize the critic
self.critic_optimizer.zero_grad()
critic_loss.backward()
self.critic_optimizer.step()
# delayed policy updates
if self.total_it % self.policy_freq == 0:
# compute the actor loss
actor_loss = -self.critic.get_q(state, self.actor(state)).mean()
# optimize the actor
self.actor_optimizer.zero_grad()
actor_loss.backward()
self.actor_optimizer.step()
# Update the frozen target models
for param, target_param in zip(self.critic.parameters(),
self.critic_target.parameters()):
target_param.data.copy_(self.tau * param.data +
(1 - self.tau) * target_param.data)
for param, target_param in zip(self.actor.parameters(),
self.actor_target.parameters()):
target_param.data.copy_(self.tau * param.data +
(1 - self.tau) * target_param.data)
def save(self, filename, directory):
torch.save(self.actor.state_dict(),
'%s/%s_actor.pth' % (directory, filename))
torch.save(self.critic.state_dict(),
'%s/%s_critic.pth' % (directory, filename))
def load(self, filename="best_avg", directory="./saves"):
self.actor.load_state_dict(
torch.load('%s/%s_actor.pth' % (directory, filename)))
self.critic.load_state_dict(
torch.load('%s/%s_critic.pth' % (directory, filename)))
class TD3:
def __init__(self,
device,
state_dim,
action_dim,
action_max,
gamma=0.99,
tau=0.005,
lr=3e-4,
policy_noise=0.2,
noise_clip=0.5,
exploration_noise=0.1,
policy_freq=2):
self.actor = Actor(state_dim, 256, action_dim, action_max).to(device)
self.target_actor = copy.deepcopy(self.actor)
self.actor_optimizer = optim.Adam(params=self.actor.parameters(),
lr=lr)
self.critic = Critic(state_dim, 256, action_dim).to(device)
self.target_critic = copy.deepcopy(self.critic)
self.critic_optimizer = optim.Adam(params=self.critic.parameters(),
lr=lr)
self.device = device
self.gamma = gamma
self.tau = tau
self.policy_noise = policy_noise
self.noise_clip = noise_clip
self.policy_freq = policy_freq
self.rollout_actor = TD3RolloutActor(state_dim, action_dim, action_max,
exploration_noise)
self.sync_rollout_actor()
self.iteration_num = 0
def train(self, replay_buffer, batch_size=256):
self.iteration_num += 1
st, nx_st, ac, rw, mask = replay_buffer.sample(batch_size)
with torch.no_grad():
noise = (torch.randn_like(ac) * self.policy_noise).clamp(
-self.noise_clip, self.noise_clip)
nx_ac = self.target_actor.forward(nx_st, noise)
target_q1, target_q2 = self.target_critic.forward(nx_st, nx_ac)
min_q = torch.min(target_q1, target_q2)
target_q = rw + mask * self.gamma * min_q
q1, q2 = self.critic.forward(st, ac)
critic_loss = F.mse_loss(q1, target_q) + F.mse_loss(q2, target_q)
self.critic.zero_grad()
critic_loss.backward()
self.critic_optimizer.step()
if self.iteration_num % self.policy_freq == 0:
actor_loss = -self.critic.q1(st, self.actor.forward(st)).mean()
self.actor.zero_grad()
actor_loss.backward()
self.actor_optimizer.step()
for param, target_param in zip(self.critic.parameters(),
self.target_critic.parameters()):
target_param.data.copy_(self.tau * param.data +
(1 - self.tau) * target_param.data)
for param, target_param in zip(self.actor.parameters(),
self.target_actor.parameters()):
target_param.data.copy_(self.tau * param.data +
(1 - self.tau) * target_param.data)
self.sync_rollout_actor()
def sync_rollout_actor(self):
for param, target_param in zip(self.actor.parameters(),
self.rollout_actor.parameters()):
target_param.data.copy_(param.data.cpu())
def save(self, path):
torch.save(self.critic.state_dict(), os.path.join(path, 'critic.pth'))
torch.save(self.target_critic.state_dict(),
os.path.join(path, 'target_critic.pth'))
torch.save(self.critic_optimizer.state_dict(),
os.path.join(path, 'critic_optimizer.pth'))
torch.save(self.actor.state_dict(), os.path.join(path, 'actor.pth'))
torch.save(self.target_actor.state_dict(),
os.path.join(path, 'target_actor.pth'))
torch.save(self.actor_optimizer.state_dict(),
os.path.join(path, 'actor_optimizer.pth'))
def load(self, path):
self.critic.load_state_dict(
torch.load(os.path.join(path, 'critic.pth')))
self.target_critic.load_state_dict(
torch.load(os.path.join(path, 'target_critic.pth')))
self.critic_optimizer.load_state_dict(
torch.load(os.path.join(path, 'critic_optimizer.pth')))
self.actor.load_state_dict(torch.load(os.path.join(path, 'actor.pth')))
self.target_actor.load_state_dict(
torch.load(os.path.join(path, 'target_actor.pth')))
self.actor_optimizer.load_state_dict(
torch.load(os.path.join(path, 'actor_optimizer.pth')))
self.sync_rollout_actor()
class TD3(object):
"""Agent class that handles the training of the networks
and provides outputs as actions.
"""
def __init__(self):
state_dim = cons.STATE_DIM.flatten().shape[0]
action_dim = cons.ACTION_DIM
self.actor = Actor(state_dim, action_dim, cons.MAX_ACTION).to(cons.DEVICE)
self.actor_target = Actor(state_dim, action_dim, cons.MAX_ACTION).to(cons.DEVICE)
self.actor_target.load_state_dict(self.actor.state_dict())
self.actor_optimizer = torch.optim.Adam(self.actor.parameters(), lr=3e-4) # or 1e-3
self.critic = Critic(state_dim, action_dim).to(cons.DEVICE)
self.critic_target = Critic(state_dim, action_dim).to(cons.DEVICE)
self.critic_target.load_state_dict(self.critic.state_dict())
self.critic_optimizer = torch.optim.Adam(self.critic.parameters(), lr=3e-4) # or 1e-3
self.total_it = 0
self.critic_loss_plot = []
self.actor_loss_plot = []
def select_action(self, state, noise=cons.POLICY_NOISE):
"""Select an appropriate action from the agent policy
Args:
state (array): current state of environment
noise (float): how much noise to add to actions
Returns:
action (list): nn action results
"""
state = torch.FloatTensor(state).to(cons.DEVICE)
action = self.actor(state)
# action space noise introduces noise to change the likelihoods of each action the agent might take
if noise != 0:
# creates tensor of gaussian noise
noise = torch.clamp(torch.randn(14, dtype=torch.float32, device='cuda') * noise,
min=-cons.NOISE_CLIP, max=cons.NOISE_CLIP)
action = action + noise
torch.clamp(action, min=cons.MIN_ACTION, max=cons.MAX_ACTION)
return action
def train(self, replay_buffer, iterations):
"""Train and update actor and critic networks
Args:
replay_buffer (ReplayBuffer): buffer for experience replay
iterations (int): how many times to run training
Return:
actor_loss (float): loss from actor network
critic_loss (float): loss from critic network
"""
for it in range(iterations):
self.total_it += 1 # keep track of the total training iterations
# Sample replay buffer (priority replay)
# choose type of replay
if cons.PRIORITY:
state, action, reward, next_state, done, weights, indexes = replay_buffer.sample(cons.BATCH_SIZE,
beta=cons.BETA_SCHED.value(it))
else:
state, action, reward, next_state, done = replay_buffer.sample(cons.BATCH_SIZE)
state = torch.from_numpy(state).float().to(cons.DEVICE) # torch.Size([100, 14])
next_state = torch.from_numpy(next_state).float().to(cons.DEVICE) # torch.Size([100, 14])
action = torch.from_numpy(action).float().to(cons.DEVICE) # torch.Size([100, 14])
reward = torch.as_tensor(reward, dtype=torch.float32).to(cons.DEVICE) # torch.Size([100])
done = torch.as_tensor(done, dtype=torch.float32).to(cons.DEVICE) # torch.Size([100])
with torch.no_grad():
# select an action according to the policy and add clipped noise
next_action = self.actor_target(next_state)
noise = torch.clamp(torch.randn((100, 14), dtype=torch.float32, device='cuda') * cons.POLICY_NOISE,
min=-cons.NOISE_CLIP, max=cons.NOISE_CLIP)
next_action = torch.clamp((next_action + noise), min=cons.MIN_ACTION, max=cons.MAX_ACTION)
# Compute the target Q value
target_q1, target_q2 = self.critic(state.float(), next_action.float())
target_q = torch.min(target_q1, target_q2)
gamma = torch.ones((100, 1), dtype=torch.float32, device='cuda')
gamma = gamma.new_full((100, 1), cons.GAMMA)
target_q = reward.unsqueeze(1) + (done.unsqueeze(1) * gamma * target_q).detach()
# get current Q estimates
current_q1, current_q2 = self.critic(state.float(), action.float())
# compute critic loss
critic_loss = F.mse_loss(current_q1, target_q) + F.mse_loss(current_q2, target_q)
cons.TD3_REPORT.write_critic_loss(self.total_it, it, critic_loss)
# optimize the critic
self.critic_optimizer.zero_grad()
critic_loss.backward()
self.critic_optimizer.step()
# using the minimum of the q values as the weight, use min to prevent overestimation
if cons.PRIORITY:
new_priorities = torch.flatten(torch.min(current_q1, current_q2))
# convert any negative priorities to a minimum value, can't have a negative priority
new_priorities = torch.clamp(new_priorities, min=0.0000001).tolist() # convert to a list for storage
replay_buffer.update_priorities(indexes, new_priorities)
# delayed policy updates
if it % cons.POLICY_FREQ == 0: # update the actor policy less frequently
# compute the actor loss
q_action = self.actor(state).float().detach()
actor_loss = -self.critic.get_q(state, q_action).mean()
cons.TD3_REPORT.write_actor_loss(self.total_it, it, actor_loss, 1)
# optimize the actor
self.actor_optimizer.zero_grad()
actor_loss.backward()
self.actor_optimizer.step()
self.actor_loss_plot.append(actor_loss.item())
# Update the frozen right_target models
for param, target_param in zip(self.critic.parameters(), self.critic_target.parameters()):
target_param.data.copy_(cons.TAU * param.data + (1 - cons.TAU) * target_param.data)
for param, target_param in zip(self.actor.parameters(), self.actor_target.parameters()):
target_param.data.copy_(cons.TAU * param.data + (1 - cons.TAU) * target_param.data)
def save(self, filename, directory):
torch.save(self.actor.state_dict(), '%s/%s_actor.pth' % (directory, filename))
torch.save(self.critic.state_dict(), '%s/%s_critic.pth' % (directory, filename))
def load(self, filename="best_avg", directory="td3/saves/shared_agent"):
self.actor.load_state_dict(torch.load('%s/%s_actor.pth' % (directory, filename)))
self.critic.load_state_dict(torch.load('%s/%s_critic.pth' % (directory, filename)))
class Agent:
def __init__(self, state_size, action_size):
self._state_size = state_size
self._action_size = action_size
# Actor network
self._actor_local = Actor(state_size, action_size).to(device)
self._actor_target = Actor(state_size, action_size).to(device)
self._actor_optimizer = optim.Adam(self._actor_local.parameters())
# Critic network
self._critic_local = Critic(state_size, action_size).to(device)
self._critic_target = Critic(state_size, action_size).to(device)
self._critic_optimizer = optim.Adam(self._critic_local.parameters())
# Memory
self._memory = Memory(BUFFER_SIZE)
# Do equal weights
self.hard_update(self._actor_local, self._actor_target)
self.hard_update(self._critic_local, self._critic_target)
def step(self, state, action, reward, next_state, done):
self._memory.push((state, action, reward, next_state, done))
if len(self._memory) > BATCH_SIZE:
for _ in range(UPDATES_PER_STEP):
samples = self._memory.sample(BATCH_SIZE)
self.learn(samples)
def act(self, state):
state = torch.from_numpy(state).float().to(device)
if binom.rvs(1, PROBABILITY_RAND_STEP):
action = np.ndarray((1, ), buffer=np.array(uniform(-1, 1).rvs()))
else:
self._actor_local.eval()
with torch.no_grad():
action = self._actor_local(state).cpu().data.numpy()
self._actor_local.train()
return np.clip(action, -1, 1)
def hard_update(self, local, target):
for target_param, local_param in zip(target.parameters(),
local.parameters()):
target_param.data.copy_(local_param.data)
def soft_update(self, local, target, tau):
for target_param, local_param in zip(target.parameters(),
local.parameters()):
target_param.data.copy_(tau * local_param.data +
(1 - tau) * target_param.data)
def learn(self, samples):
states, actions, rewards, next_states, dones = samples
actions_next = self._actor_target(next_states)
Q_targets_next = self._critic_target(next_states, actions_next)
Q_targets = rewards + (GAMMA * Q_targets_next * (1 - dones))
Q_expected = self._critic_local(states, actions)
critic_loss = F.mse_loss(Q_expected, Q_targets)
self._critic_optimizer.zero_grad()
critic_loss.backward()
self._critic_optimizer.step()
actions_pred = self._actor_local(states)
actor_loss = -self._critic_local(states, actions_pred).mean()
self._actor_optimizer.zero_grad()
actor_loss.backward()
self._actor_optimizer.step()
self.soft_update(self._critic_local, self._critic_target, TAU)
self.soft_update(self._actor_local, self._actor_target, TAU)
def save(self):
torch.save(self._actor_local.state_dict(), ACTOR_PATH)
torch.save(self._critic_local.state_dict(), CRITIC_PATH)
def load(self):
self._actor_local.load_state_dict(torch.load(ACTOR_PATH))
self._actor_local.eval()
self._critic_local.load_state_dict(torch.load(CRITIC_PATH))
self._critic_local.eval()
