class DQfDAgent(DQNAgent):
"""DQN interacting with environment.
Attribute:
memory (PrioritizedReplayBuffer): replay memory
"""
# pylint: disable=attribute-defined-outside-init
def _initialize(self):
"""Initialize non-common things."""
if not self.args.test:
# load demo replay memory
demos = self._load_demos()
if self.use_n_step:
demos, demos_n_step = common_utils.get_n_step_info_from_demo(
demos, self.hyper_params.n_step, self.hyper_params.gamma)
self.memory_n = ReplayBuffer(
buffer_size=self.hyper_params.buffer_size,
n_step=self.hyper_params.n_step,
gamma=self.hyper_params.gamma,
demo=demos_n_step,
)
# replay memory
self.memory = PrioritizedReplayBuffer(
self.hyper_params.buffer_size,
self.hyper_params.batch_size,
demo=demos,
alpha=self.hyper_params.per_alpha,
epsilon_d=self.hyper_params.per_eps_demo,
)
def _load_demos(self) -> list:
"""Load expert's demonstrations."""
# load demo replay memory
with open(self.args.demo_path, "rb") as f:
demos = pickle.load(f)
return demos
def update_model(self) -> Tuple[torch.Tensor, ...]:
"""Train the model after each episode."""
experiences_1 = self.memory.sample()
weights, indices, eps_d = experiences_1[-3:]
actions = experiences_1[1]
# 1 step loss
gamma = self.hyper_params.gamma
dq_loss_element_wise, q_values = self._get_dqn_loss(
experiences_1, gamma)
dq_loss = torch.mean(dq_loss_element_wise * weights)
# n step loss
if self.use_n_step:
experiences_n = self.memory_n.sample(indices)
gamma = self.hyper_params.gamma**self.hyper_params.n_step
dq_loss_n_element_wise, q_values_n = self._get_dqn_loss(
experiences_n, gamma)
# to update loss and priorities
q_values = 0.5 * (q_values + q_values_n)
dq_loss_element_wise += dq_loss_n_element_wise * self.hyper_params.lambda1
dq_loss = torch.mean(dq_loss_element_wise * weights)
# supervised loss using demo for only demo transitions
demo_idxs = np.where(eps_d != 0.0)
n_demo = demo_idxs[0].size
if n_demo != 0: # if 1 or more demos are sampled
# get margin for each demo transition
action_idxs = actions[demo_idxs].long()
margin = torch.ones(q_values.size()) * self.hyper_params.margin
margin[demo_idxs, action_idxs] = 0.0 # demo actions have 0 margins
margin = margin.to(device)
# calculate supervised loss
demo_q_values = q_values[demo_idxs, action_idxs].squeeze()
supervised_loss = torch.max(q_values + margin, dim=-1)[0]
supervised_loss = supervised_loss[demo_idxs] - demo_q_values
supervised_loss = torch.mean(
supervised_loss) * self.hyper_params.lambda2
else: # no demo sampled
supervised_loss = torch.zeros(1, device=device)
# q_value regularization
q_regular = torch.norm(q_values, 2).mean() * self.hyper_params.w_q_reg
# total loss
loss = dq_loss + supervised_loss + q_regular
# train dqn
self.dqn_optim.zero_grad()
loss.backward()
clip_grad_norm_(self.dqn.parameters(), self.hyper_params.gradient_clip)
self.dqn_optim.step()
# update target networks
common_utils.soft_update(self.dqn, self.dqn_target,
self.hyper_params.tau)
# update priorities in PER
loss_for_prior = dq_loss_element_wise.detach().cpu().numpy().squeeze()
new_priorities = loss_for_prior + self.hyper_params.per_eps
new_priorities += eps_d
self.memory.update_priorities(indices, new_priorities)
# increase beta
fraction = min(float(self.i_episode) / self.args.episode_num, 1.0)
self.per_beta: float = self.per_beta + fraction * (1.0 - self.per_beta)
if self.hyper_params.use_noisy_net:
self.dqn.reset_noise()
self.dqn_target.reset_noise()
return (
loss.item(),
dq_loss.item(),
supervised_loss.item(),
q_values.mean().item(),
n_demo,
)
def write_log(self, log_value: tuple):
"""Write log about loss and score"""
i, avg_loss, score, avg_time_cost = log_value
print(
"[INFO] episode %d, episode step: %d, total step: %d, total score: %f\n"
"epsilon: %f, total loss: %f, dq loss: %f, supervised loss: %f\n"
"avg q values: %f, demo num in minibatch: %d (spent %.6f sec/step)\n"
% (
i,
self.episode_step,
self.total_step,
score,
self.epsilon,
avg_loss[0],
avg_loss[1],
avg_loss[2],
avg_loss[3],
avg_loss[4],
avg_time_cost,
))
if self.args.log:
wandb.log({
"score": score,
"epsilon": self.epsilon,
"total loss": avg_loss[0],
"dq loss": avg_loss[1],
"supervised loss": avg_loss[2],
"avg q values": avg_loss[3],
"demo num in minibatch": avg_loss[4],
"time per each step": avg_time_cost,
})
def pretrain(self):
"""Pretraining steps."""
pretrain_loss = list()
pretrain_step = self.hyper_params.pretrain_step
print("[INFO] Pre-Train %d step." % pretrain_step)
for i_step in range(1, pretrain_step + 1):
t_begin = time.time()
loss = self.update_model()
t_end = time.time()
pretrain_loss.append(loss) # for logging
# logging
if i_step == 1 or i_step % 100 == 0:
avg_loss = np.vstack(pretrain_loss).mean(axis=0)
pretrain_loss.clear()
log_value = (0, avg_loss, 0.0, t_end - t_begin)
self.write_log(log_value)
print("[INFO] Pre-Train Complete!\n")
class DQNAgent(Agent):
"""DQN interacting with environment.
Attribute:
env (gym.Env): openAI Gym environment
args (argparse.Namespace): arguments including hyperparameters and training settings
hyper_params (ConfigDict): hyper-parameters
network_cfg (ConfigDict): config of network for training agent
optim_cfg (ConfigDict): config of optimizer
state_dim (int): state size of env
action_dim (int): action size of env
memory (PrioritizedReplayBuffer): replay memory
dqn (nn.Module): actor model to select actions
dqn_target (nn.Module): target actor model to select actions
dqn_optim (Optimizer): optimizer for training actor
curr_state (np.ndarray): temporary storage of the current state
total_step (int): total step number
episode_step (int): step number of the current episode
i_episode (int): current episode number
epsilon (float): parameter for epsilon greedy policy
n_step_buffer (deque): n-size buffer to calculate n-step returns
per_beta (float): beta parameter for prioritized replay buffer
use_conv (bool): whether or not to use convolution layer
use_n_step (bool): whether or not to use n-step returns
"""
def __init__(
self,
env: gym.Env,
args: argparse.Namespace,
log_cfg: ConfigDict,
hyper_params: ConfigDict,
network_cfg: ConfigDict,
optim_cfg: ConfigDict,
):
"""Initialize."""
Agent.__init__(self, env, args, log_cfg)
self.curr_state = np.zeros(1)
self.episode_step = 0
self.total_step = 0
self.i_episode = 0
self.hyper_params = hyper_params
self.network_cfg = network_cfg
self.optim_cfg = optim_cfg
self.state_dim = self.env.observation_space.shape
self.action_dim = self.env.action_space.n
self.per_beta = hyper_params.per_beta
self.use_conv = len(self.state_dim) > 1
self.use_n_step = hyper_params.n_step > 1
if hyper_params.use_noisy_net:
self.max_epsilon = 0.0
self.min_epsilon = 0.0
self.epsilon = 0.0
else:
self.max_epsilon = hyper_params.max_epsilon
self.min_epsilon = hyper_params.min_epsilon
self.epsilon = hyper_params.max_epsilon
self._initialize()
self._init_network()
# pylint: disable=attribute-defined-outside-init
def _initialize(self):
"""Initialize non-common things."""
if not self.args.test:
# replay memory for a single step
self.memory = PrioritizedReplayBuffer(
self.hyper_params.buffer_size,
self.hyper_params.batch_size,
alpha=self.hyper_params.per_alpha,
)
# replay memory for multi-steps
if self.use_n_step:
self.memory_n = ReplayBuffer(
self.hyper_params.buffer_size,
n_step=self.hyper_params.n_step,
gamma=self.hyper_params.gamma,
)
# pylint: disable=attribute-defined-outside-init
def _init_network(self):
"""Initialize networks and optimizers."""
if self.use_conv:
# create CNN
self.dqn = dqn_utils.get_cnn_model(self.hyper_params,
self.action_dim, self.state_dim,
self.network_cfg)
self.dqn_target = dqn_utils.get_cnn_model(self.hyper_params,
self.action_dim,
self.state_dim,
self.network_cfg)
else:
# create FC
fc_input_size = self.state_dim[0]
self.dqn = dqn_utils.get_fc_model(
self.hyper_params,
fc_input_size,
self.action_dim,
self.network_cfg.hidden_sizes,
)
self.dqn_target = dqn_utils.get_fc_model(
self.hyper_params,
fc_input_size,
self.action_dim,
self.network_cfg.hidden_sizes,
)
self.dqn_target.load_state_dict(self.dqn.state_dict())
# create optimizer
self.dqn_optim = optim.Adam(
self.dqn.parameters(),
lr=self.optim_cfg.lr_dqn,
weight_decay=self.optim_cfg.weight_decay,
eps=self.optim_cfg.adam_eps,
)
# load the optimizer and model parameters
if self.args.load_from is not None:
self.load_params(self.args.load_from)
def select_action(self, state: np.ndarray) -> np.ndarray:
"""Select an action from the input space."""
self.curr_state = state
# epsilon greedy policy
# pylint: disable=comparison-with-callable
if not self.args.test and self.epsilon > np.random.random():
selected_action = np.array(self.env.action_space.sample())
else:
state = self._preprocess_state(state)
selected_action = self.dqn(state).argmax()
selected_action = selected_action.detach().cpu().numpy()
return selected_action
# pylint: disable=no-self-use
def _preprocess_state(self, state: np.ndarray) -> torch.Tensor:
"""Preprocess state so that actor selects an action."""
state = torch.FloatTensor(state).to(device)
return state
def step(self,
action: np.ndarray) -> Tuple[np.ndarray, np.float64, bool, dict]:
"""Take an action and return the response of the env."""
next_state, reward, done, info = self.env.step(action)
if not self.args.test:
# if the last state is not a terminal state, store done as false
done_bool = (False if self.episode_step
== self.args.max_episode_steps else done)
transition = (self.curr_state, action, reward, next_state,
done_bool)
self._add_transition_to_memory(transition)
return next_state, reward, done, info
def _add_transition_to_memory(self, transition: Tuple[np.ndarray, ...]):
"""Add 1 step and n step transitions to memory."""
# add n-step transition
if self.use_n_step:
transition = self.memory_n.add(transition)
# add a single step transition
# if transition is not an empty tuple
if transition:
self.memory.add(transition)
def _get_dqn_loss(self, experiences: Tuple[torch.Tensor, ...],
gamma: float) -> Tuple[torch.Tensor, torch.Tensor]:
"""Return element-wise dqn loss and Q-values."""
if self.hyper_params.use_dist_q == "IQN":
return dqn_utils.calculate_iqn_loss(
model=self.dqn,
target_model=self.dqn_target,
experiences=experiences,
gamma=gamma,
batch_size=self.hyper_params.batch_size,
n_tau_samples=self.hyper_params.n_tau_samples,
n_tau_prime_samples=self.hyper_params.n_tau_prime_samples,
kappa=self.hyper_params.kappa,
)
elif self.hyper_params.use_dist_q == "C51":
return dqn_utils.calculate_c51_loss(
model=self.dqn,
target_model=self.dqn_target,
experiences=experiences,
gamma=gamma,
batch_size=self.hyper_params.batch_size,
v_min=self.hyper_params.v_min,
v_max=self.hyper_params.v_max,
atom_size=self.hyper_params.atoms,
)
else:
return dqn_utils.calculate_dqn_loss(
model=self.dqn,
target_model=self.dqn_target,
experiences=experiences,
gamma=gamma,
)
def update_model(self) -> Tuple[torch.Tensor, ...]:
"""Train the model after each episode."""
# 1 step loss
experiences_1 = self.memory.sample(self.per_beta)
weights, indices = experiences_1[-3:-1]
gamma = self.hyper_params.gamma
dq_loss_element_wise, q_values = self._get_dqn_loss(
experiences_1, gamma)
dq_loss = torch.mean(dq_loss_element_wise * weights)
# n step loss
if self.use_n_step:
experiences_n = self.memory_n.sample(indices)
gamma = self.hyper_params.gamma**self.hyper_params.n_step
dq_loss_n_element_wise, q_values_n = self._get_dqn_loss(
experiences_n, gamma)
# to update loss and priorities
q_values = 0.5 * (q_values + q_values_n)
dq_loss_element_wise += dq_loss_n_element_wise * self.hyper_params.w_n_step
dq_loss = torch.mean(dq_loss_element_wise * weights)
# q_value regularization
q_regular = torch.norm(q_values, 2).mean() * self.hyper_params.w_q_reg
# total loss
loss = dq_loss + q_regular
self.dqn_optim.zero_grad()
loss.backward()
clip_grad_norm_(self.dqn.parameters(), self.hyper_params.gradient_clip)
self.dqn_optim.step()
# update target networks
common_utils.soft_update(self.dqn, self.dqn_target,
self.hyper_params.tau)
# update priorities in PER
loss_for_prior = dq_loss_element_wise.detach().cpu().numpy()
new_priorities = loss_for_prior + self.hyper_params.per_eps
self.memory.update_priorities(indices, new_priorities)
# increase beta
fraction = min(float(self.i_episode) / self.args.episode_num, 1.0)
self.per_beta = self.per_beta + fraction * (1.0 - self.per_beta)
if self.hyper_params.use_noisy_net:
self.dqn.reset_noise()
self.dqn_target.reset_noise()
return loss.item(), q_values.mean().item()
def load_params(self, path: str):
"""Load model and optimizer parameters."""
Agent.load_params(self, path)
params = torch.load(path)
self.dqn.load_state_dict(params["dqn_state_dict"])
self.dqn_target.load_state_dict(params["dqn_target_state_dict"])
self.dqn_optim.load_state_dict(params["dqn_optim_state_dict"])
print("[INFO] loaded the model and optimizer from", path)
def save_params(self, n_episode: int): # type: ignore
"""Save model and optimizer parameters."""
params = {
"dqn_state_dict": self.dqn.state_dict(),
"dqn_target_state_dict": self.dqn_target.state_dict(),
"dqn_optim_state_dict": self.dqn_optim.state_dict(),
}
Agent.save_params(self, params, n_episode)
def write_log(self, log_value: tuple):
"""Write log about loss and score"""
i, loss, score, avg_time_cost = log_value
print(
"[INFO] episode %d, episode step: %d, total step: %d, total score: %f\n"
"epsilon: %f, loss: %f, avg q-value: %f (spent %.6f sec/step)\n" %
(
i,
self.episode_step,
self.total_step,
score,
self.epsilon,
loss[0],
loss[1],
avg_time_cost,
))
if self.args.log:
wandb.log({
"score": score,
"epsilon": self.epsilon,
"dqn loss": loss[0],
"avg q values": loss[1],
"time per each step": avg_time_cost,
})
# pylint: disable=no-self-use, unnecessary-pass
def pretrain(self):
"""Pretraining steps."""
pass
def train(self):
"""Train the agent."""
# logger
if self.args.log:
self.set_wandb()
# wandb.watch([self.dqn], log="parameters")
# pre-training if needed
self.pretrain()
for self.i_episode in range(1, self.args.episode_num + 1):
state = self.env.reset()
self.episode_step = 0
losses = list()
done = False
score = 0
t_begin = time.time()
while not done:
if self.args.render and self.i_episode >= self.args.render_after:
self.env.render()
action = self.select_action(state)
next_state, reward, done, _ = self.step(action)
self.total_step += 1
self.episode_step += 1
if len(self.memory) >= self.hyper_params.update_starts_from:
if self.total_step % self.hyper_params.train_freq == 0:
for _ in range(self.hyper_params.multiple_update):
loss = self.update_model()
losses.append(loss) # for logging
# decrease epsilon
self.epsilon = max(
self.epsilon - (self.max_epsilon - self.min_epsilon) *
self.hyper_params.epsilon_decay,
self.min_epsilon,
)
state = next_state
score += reward
t_end = time.time()
avg_time_cost = (t_end - t_begin) / self.episode_step
if losses:
avg_loss = np.vstack(losses).mean(axis=0)
log_value = (self.i_episode, avg_loss, score, avg_time_cost)
self.write_log(log_value)
if self.i_episode % self.args.save_period == 0:
self.save_params(self.i_episode)
self.interim_test()
# termination
self.env.close()
self.save_params(self.i_episode)
self.interim_test()
class DDPGfDAgent(DDPGAgent):
"""ActorCritic interacting with environment.
Attributes:
memory (PrioritizedReplayBuffer): replay memory
per_beta (float): beta parameter for prioritized replay buffer
use_n_step (bool): whether or not to use n-step returns
"""
# pylint: disable=attribute-defined-outside-init
def _initialize(self):
"""Initialize non-common things."""
self.per_beta = self.hyper_params.per_beta
self.use_n_step = self.hyper_params.n_step > 1
if not self.args.test:
# load demo replay memory
with open(self.args.demo_path, "rb") as f:
demos = pickle.load(f)
if self.use_n_step:
demos, demos_n_step = common_utils.get_n_step_info_from_demo(
demos, self.hyper_params.n_step, self.hyper_params.gamma
)
# replay memory for multi-steps
self.memory_n = ReplayBuffer(
buffer_size=self.hyper_params.buffer_size,
batch_size=self.hyper_params.batch_size,
n_step=self.hyper_params.n_step,
gamma=self.hyper_params.gamma,
demo=demos_n_step,
)
# replay memory for a single step
self.memory = PrioritizedReplayBuffer(
self.hyper_params.buffer_size,
self.hyper_params.batch_size,
demo=demos,
alpha=self.hyper_params.per_alpha,
epsilon_d=self.hyper_params.per_eps_demo,
)
def _add_transition_to_memory(self, transition: Tuple[np.ndarray, ...]):
"""Add 1 step and n step transitions to memory."""
# add n-step transition
if self.use_n_step:
transition = self.memory_n.add(transition)
# add a single step transition
# if transition is not an empty tuple
if transition:
self.memory.add(transition)
def _get_critic_loss(
self, experiences: Tuple[torch.Tensor, ...], gamma: float
) -> torch.Tensor:
"""Return element-wise critic loss."""
states, actions, rewards, next_states, dones = experiences[:5]
# G_t = r + gamma * v(s_{t+1}) if state != Terminal
# = r otherwise
masks = 1 - dones
next_actions = self.actor_target(next_states)
next_states_actions = torch.cat((next_states, next_actions), dim=-1)
next_values = self.critic_target(next_states_actions)
curr_returns = rewards + gamma * next_values * masks
curr_returns = curr_returns.to(device).detach()
# train critic
values = self.critic(torch.cat((states, actions), dim=-1))
critic_loss_element_wise = (values - curr_returns).pow(2)
return critic_loss_element_wise
def update_model(self) -> Tuple[torch.Tensor, ...]:
"""Train the model after each episode."""
experiences_1 = self.memory.sample(self.per_beta)
states, actions = experiences_1[:2]
weights, indices, eps_d = experiences_1[-3:]
gamma = self.hyper_params.gamma
# train critic
gradient_clip_ac = self.hyper_params.gradient_clip_ac
gradient_clip_cr = self.hyper_params.gradient_clip_cr
critic_loss_element_wise = self._get_critic_loss(experiences_1, gamma)
critic_loss = torch.mean(critic_loss_element_wise * weights)
if self.use_n_step:
experiences_n = self.memory_n.sample(indices)
gamma = gamma ** self.hyper_params.n_step
critic_loss_n_element_wise = self._get_critic_loss(experiences_n, gamma)
# to update loss and priorities
critic_loss_element_wise += (
critic_loss_n_element_wise * self.hyper_params.lambda1
)
critic_loss = torch.mean(critic_loss_element_wise * weights)
self.critic_optim.zero_grad()
critic_loss.backward()
nn.utils.clip_grad_norm_(self.critic.parameters(), gradient_clip_cr)
self.critic_optim.step()
# train actor
actions = self.actor(states)
actor_loss_element_wise = -self.critic(torch.cat((states, actions), dim=-1))
actor_loss = torch.mean(actor_loss_element_wise * weights)
self.actor_optim.zero_grad()
actor_loss.backward()
nn.utils.clip_grad_norm_(self.actor.parameters(), gradient_clip_ac)
self.actor_optim.step()
# update target networks
common_utils.soft_update(self.actor, self.actor_target, self.hyper_params.tau)
common_utils.soft_update(self.critic, self.critic_target, self.hyper_params.tau)
# update priorities
new_priorities = critic_loss_element_wise
new_priorities += self.hyper_params.lambda3 * actor_loss_element_wise.pow(2)
new_priorities += self.hyper_params.per_eps
new_priorities = new_priorities.data.cpu().numpy().squeeze()
new_priorities += eps_d
self.memory.update_priorities(indices, new_priorities)
# increase beta
fraction = min(float(self.i_episode) / self.args.episode_num, 1.0)
self.per_beta = self.per_beta + fraction * (1.0 - self.per_beta)
return actor_loss.item(), critic_loss.item()
def pretrain(self):
"""Pretraining steps."""
pretrain_loss = list()
pretrain_step = self.hyper_params.pretrain_step
print("[INFO] Pre-Train %d step." % pretrain_step)
for i_step in range(1, pretrain_step + 1):
t_begin = time.time()
loss = self.update_model()
t_end = time.time()
pretrain_loss.append(loss) # for logging
# logging
if i_step == 1 or i_step % 100 == 0:
avg_loss = np.vstack(pretrain_loss).mean(axis=0)
pretrain_loss.clear()
log_value = (0, avg_loss, 0, t_end - t_begin)
self.write_log(log_value)
print("[INFO] Pre-Train Complete!\n")
class SACfDAgent(SACAgent):
"""SAC agent interacting with environment.
Attrtibutes:
memory (PrioritizedReplayBuffer): replay memory
beta (float): beta parameter for prioritized replay buffer
use_n_step (bool): whether or not to use n-step returns
"""
# pylint: disable=attribute-defined-outside-init
def _initialize(self):
"""Initialize non-common things."""
self.per_beta = self.hyper_params.per_beta
self.use_n_step = self.hyper_params.n_step > 1
if not self.args.test:
# load demo replay memory
with open(self.args.demo_path, "rb") as f:
demos = pickle.load(f)
if self.use_n_step:
demos, demos_n_step = common_utils.get_n_step_info_from_demo(
demos, self.hyper_params.n_step, self.hyper_params.gamma)
# replay memory for multi-steps
self.memory_n = ReplayBuffer(
buffer_size=self.hyper_params.buffer_size,
batch_size=self.hyper_params.batch_size,
n_step=self.hyper_params.n_step,
gamma=self.hyper_params.gamma,
demo=demos_n_step,
)
# replay memory
self.memory = PrioritizedReplayBuffer(
self.hyper_params.buffer_size,
self.hyper_params.batch_size,
demo=demos,
alpha=self.hyper_params.per_alpha,
epsilon_d=self.hyper_params.per_eps_demo,
)
def _add_transition_to_memory(self, transition: Tuple[np.ndarray, ...]):
"""Add 1 step and n step transitions to memory."""
# add n-step transition
if self.use_n_step:
transition = self.memory_n.add(transition)
# add a single step transition
# if transition is not an empty tuple
if transition:
self.memory.add(transition)
# pylint: disable=too-many-statements
def update_model(self) -> Tuple[torch.Tensor, ...]:
"""Train the model after each episode."""
self.update_step += 1
experiences = self.memory.sample(self.per_beta)
(
states,
actions,
rewards,
next_states,
dones,
weights,
indices,
eps_d,
) = experiences
new_actions, log_prob, pre_tanh_value, mu, std = self.actor(states)
# train alpha
if self.hyper_params.auto_entropy_tuning:
alpha_loss = torch.mean(
(-self.log_alpha *
(log_prob + self.target_entropy).detach()) * weights)
self.alpha_optim.zero_grad()
alpha_loss.backward()
self.alpha_optim.step()
alpha = self.log_alpha.exp()
else:
alpha_loss = torch.zeros(1)
alpha = self.hyper_params.w_entropy
# Q function loss
masks = 1 - dones
gamma = self.hyper_params.gamma
states_actions = torch.cat((states, actions), dim=-1)
q_1_pred = self.qf_1(states_actions)
q_2_pred = self.qf_2(states_actions)
v_target = self.vf_target(next_states)
q_target = rewards + self.hyper_params.gamma * v_target * masks
qf_1_loss = torch.mean((q_1_pred - q_target.detach()).pow(2) * weights)
qf_2_loss = torch.mean((q_2_pred - q_target.detach()).pow(2) * weights)
if self.use_n_step:
experiences_n = self.memory_n.sample(indices)
_, _, rewards, next_states, dones = experiences_n
gamma = gamma**self.hyper_params.n_step
masks = 1 - dones
v_target = self.vf_target(next_states)
q_target = rewards + gamma * v_target * masks
qf_1_loss_n = torch.mean(
(q_1_pred - q_target.detach()).pow(2) * weights)
qf_2_loss_n = torch.mean(
(q_2_pred - q_target.detach()).pow(2) * weights)
# to update loss and priorities
qf_1_loss = qf_1_loss + qf_1_loss_n * self.hyper_params.lambda1
qf_2_loss = qf_2_loss + qf_2_loss_n * self.hyper_params.lambda1
# V function loss
states_actions = torch.cat((states, new_actions), dim=-1)
v_pred = self.vf(states)
q_pred = torch.min(self.qf_1(states_actions),
self.qf_2(states_actions))
v_target = (q_pred - alpha * log_prob).detach()
vf_loss_element_wise = (v_pred - v_target).pow(2)
vf_loss = torch.mean(vf_loss_element_wise * weights)
# train Q functions
self.qf_1_optim.zero_grad()
qf_1_loss.backward()
self.qf_1_optim.step()
self.qf_2_optim.zero_grad()
qf_2_loss.backward()
self.qf_2_optim.step()
# train V function
self.vf_optim.zero_grad()
vf_loss.backward()
self.vf_optim.step()
if self.update_step % self.hyper_params.policy_update_freq == 0:
# actor loss
advantage = q_pred - v_pred.detach()
actor_loss_element_wise = alpha * log_prob - advantage
actor_loss = torch.mean(actor_loss_element_wise * weights)
# regularization
mean_reg = self.hyper_params.w_mean_reg * mu.pow(2).mean()
std_reg = self.hyper_params.w_std_reg * std.pow(2).mean()
pre_activation_reg = self.hyper_params.w_pre_activation_reg * (
pre_tanh_value.pow(2).sum(dim=-1).mean())
actor_reg = mean_reg + std_reg + pre_activation_reg
# actor loss + regularization
actor_loss += actor_reg
# train actor
self.actor_optim.zero_grad()
actor_loss.backward()
self.actor_optim.step()
# update target networks
common_utils.soft_update(self.vf, self.vf_target,
self.hyper_params.tau)
# update priorities
new_priorities = vf_loss_element_wise
new_priorities += self.hyper_params.lambda3 * actor_loss_element_wise.pow(
2)
new_priorities += self.hyper_params.per_eps
new_priorities = new_priorities.data.cpu().numpy().squeeze()
new_priorities += eps_d
self.memory.update_priorities(indices, new_priorities)
# increase beta
fraction = min(float(self.i_episode) / self.args.episode_num, 1.0)
self.per_beta = self.per_beta + fraction * (1.0 - self.per_beta)
else:
actor_loss = torch.zeros(1)
return (
actor_loss.item(),
qf_1_loss.item(),
qf_2_loss.item(),
vf_loss.item(),
alpha_loss.item(),
)
def pretrain(self):
"""Pretraining steps."""
pretrain_loss = list()
pretrain_step = self.hyper_params.pretrain_step
print("[INFO] Pre-Train %d steps." % pretrain_step)
for i_step in range(1, pretrain_step + 1):
t_begin = time.time()
loss = self.update_model()
t_end = time.time()
pretrain_loss.append(loss) # for logging
# logging
if i_step == 1 or i_step % 100 == 0:
avg_loss = np.vstack(pretrain_loss).mean(axis=0)
pretrain_loss.clear()
log_value = (
0,
avg_loss,
0,
self.hyper_params.policy_update_freq,
t_end - t_begin,
)
self.write_log(log_value)
print("[INFO] Pre-Train Complete!\n")
class PERDDPGAgent(DDPGAgent):
"""ActorCritic interacting with environment.
Attributes:
memory (PrioritizedReplayBuffer): replay memory
per_beta (float): beta parameter for prioritized replay buffer
"""
# pylint: disable=attribute-defined-outside-init
def _initialize(self):
"""Initialize non-common things."""
self.per_beta = self.hyper_params.per_beta
if not self.args.test:
# replay memory
self.memory = PrioritizedReplayBuffer(
self.hyper_params.buffer_size,
self.hyper_params.batch_size,
alpha=self.hyper_params.per_alpha,
)
def update_model(self) -> Tuple[torch.Tensor, ...]:
"""Train the model after each episode."""
experiences = self.memory.sample(self.per_beta)
states, actions, rewards, next_states, dones, weights, indices, _ = experiences
# G_t = r + gamma * v(s_{t+1}) if state != Terminal
# = r otherwise
masks = 1 - dones
next_actions = self.actor_target(next_states)
next_values = self.critic_target(torch.cat((next_states, next_actions), dim=-1))
curr_returns = rewards + self.hyper_params.gamma * next_values * masks
curr_returns = curr_returns.to(device).detach()
# train critic
gradient_clip_ac = self.hyper_params.gradient_clip_ac
gradient_clip_cr = self.hyper_params.gradient_clip_cr
values = self.critic(torch.cat((states, actions), dim=-1))
critic_loss_element_wise = (values - curr_returns).pow(2)
critic_loss = torch.mean(critic_loss_element_wise * weights)
self.critic_optim.zero_grad()
critic_loss.backward()
nn.utils.clip_grad_norm_(self.critic.parameters(), gradient_clip_cr)
self.critic_optim.step()
# train actor
actions = self.actor(states)
actor_loss_element_wise = -self.critic(torch.cat((states, actions), dim=-1))
actor_loss = torch.mean(actor_loss_element_wise * weights)
self.actor_optim.zero_grad()
actor_loss.backward()
nn.utils.clip_grad_norm_(self.actor.parameters(), gradient_clip_ac)
self.actor_optim.step()
# update target networks
common_utils.soft_update(self.actor, self.actor_target, self.hyper_params.tau)
common_utils.soft_update(self.critic, self.critic_target, self.hyper_params.tau)
# update priorities in PER
new_priorities = critic_loss_element_wise
new_priorities = new_priorities.data.cpu().numpy() + self.hyper_params.per_eps
self.memory.update_priorities(indices, new_priorities)
# increase beta
fraction = min(float(self.i_episode) / self.args.episode_num, 1.0)
self.per_beta = self.per_beta + fraction * (1.0 - self.per_beta)
return actor_loss.item(), critic_loss.item()