info["episode"]["r"], global_step)
break
# TRY NOT TO MODIFY: save data to reply buffer; handle `terminal_observation`
real_next_obs = next_obs.copy()
for idx, d in enumerate(dones):
if d:
real_next_obs[idx] = infos[idx]["terminal_observation"]
rb.add(obs, real_next_obs, actions, rewards, dones, infos)
# TRY NOT TO MODIFY: CRUCIAL step easy to overlook
obs = next_obs
# ALGO LOGIC: training.
if global_step > args.learning_starts:
data = rb.sample(args.batch_size)
with torch.no_grad():
clipped_noise = (torch.randn_like(torch.Tensor(actions[0])) *
args.policy_noise).clamp(
-args.noise_clip, args.noise_clip)
next_state_actions = (
target_actor.forward(data.next_observations) +
clipped_noise.to(device)).clamp(
envs.single_action_space.low[0],
envs.single_action_space.high[0])
qf1_next_target = qf1_target.forward(data.next_observations,
next_state_actions)
qf2_next_target = qf2_target.forward(data.next_observations,
next_state_actions)
min_qf_next_target = torch.min(qf1_next_target,
class AWAC(OffPolicyAlgorithm):
"""
Soft Actor-Critic (SAC)
Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor,
This implementation borrows code from original implementation (https://github.com/haarnoja/sac)
from OpenAI Spinning Up (https://github.com/openai/spinningup), from the softlearning repo
(https://github.com/rail-berkeley/softlearning/)
and from Stable Baselines (https://github.com/hill-a/stable-baselines)
Paper: https://arxiv.org/abs/1801.01290
Introduction to SAC: https://spinningup.openai.com/en/latest/algorithms/sac.html
Note: we use double q target and not value target as discussed
in https://github.com/hill-a/stable-baselines/issues/270
:param policy: (SACPolicy or str) The policy model to use (MlpPolicy, CnnPolicy, ...)
:param env: (GymEnv or str) The environment to learn from (if registered in Gym, can be str)
:param learning_rate: (float or callable) learning rate for adam optimizer,
the same learning rate will be used for all networks (Q-Values, Actor and Value function)
it can be a function of the current progress remaining (from 1 to 0)
:param buffer_size: (int) size of the replay buffer
:param learning_starts: (int) how many steps of the model to collect transitions for before learning starts
:param batch_size: (int) Minibatch size for each gradient update
:param tau: (float) the soft update coefficient ("Polyak update", between 0 and 1)
:param gamma: (float) the discount factor
:param train_freq: (int) Update the model every ``train_freq`` steps.
:param gradient_steps: (int) How many gradient update after each step
:param n_episodes_rollout: (int) Update the model every ``n_episodes_rollout`` episodes.
Note that this cannot be used at the same time as ``train_freq``
:param action_noise: (ActionNoise) the action noise type (None by default), this can help
for hard exploration problem. Cf common.noise for the different action noise type.
:param ent_coef: (str or float) Entropy regularization coefficient. (Equivalent to
inverse of reward scale in the original SAC paper.) Controlling exploration/exploitation trade-off.
Set it to 'auto' to learn it automatically (and 'auto_0.1' for using 0.1 as initial value)
:param target_update_interval: (int) update the target network every ``target_network_update_freq`` steps.
:param target_entropy: (str or float) target entropy when learning ``ent_coef`` (``ent_coef = 'auto'``)
:param create_eval_env: (bool) Whether to create a second environment that will be
used for evaluating the agent periodically. (Only available when passing string for the environment)
:param policy_kwargs: (dict) additional arguments to be passed to the policy on creation
:param verbose: (int) the verbosity level: 0 no output, 1 info, 2 debug
:param seed: (int) Seed for the pseudo random generators
:param device: (str or th.device) Device (cpu, cuda, ...) on which the code should be run.
Setting it to auto, the code will be run on the GPU if possible.
:param _init_setup_model: (bool) Whether or not to build the network at the creation of the instance
"""
def __init__(self,
policy: Union[str, Type[AWACPolicy]],
env: Union[GymEnv, str],
learning_rate: Union[float, Callable] = 3e-4,
buffer_size: int = int(1e6),
learning_starts: int = 100,
batch_size: int = 256,
tau: float = 0.005,
gamma: float = 0.99,
train_freq: int = 1,
gradient_steps: int = 1,
n_episodes_rollout: int = -1,
action_noise: Optional[ActionNoise] = None,
ent_coef: Union[str, float] = 'auto',
target_update_interval: int = 1,
target_entropy: Union[str, float] = 'auto',
awr_use_mle_for_vf: bool = True,
beta: int = 50,
tensorboard_log: Optional[str] = None,
create_eval_env: bool = False,
policy_kwargs: Dict[str, Any] = None,
verbose: int = 0,
seed: Optional[int] = None,
device: Union[th.device, str] = 'auto',
_init_setup_model: bool = True):
super().__init__(policy,
env,
AWACPolicy,
learning_rate,
buffer_size,
learning_starts,
batch_size,
policy_kwargs,
tensorboard_log,
verbose,
device,
create_eval_env=create_eval_env,
seed=seed,
use_sde=False,
sde_sample_freq=-1,
use_sde_at_warmup=False)
self.target_entropy = target_entropy
self.log_ent_coef = None # type: Optional[th.Tensor]
self.target_update_interval = target_update_interval
self.tau = tau
# Entropy coefficient / Entropy temperature
# Inverse of the reward scale
self.ent_coef = ent_coef
self.target_update_interval = target_update_interval
self.train_freq = train_freq
self.gradient_steps = gradient_steps
self.n_episodes_rollout = n_episodes_rollout
self.action_noise = action_noise
self.gamma = gamma
self.ent_coef_optimizer = None
self.awr_use_mle_for_vf = awr_use_mle_for_vf
self.beta = beta
self.bc_buffer = None
if _init_setup_model:
self._setup_model()
def _setup_model(self) -> None:
super()._setup_model()
self._create_aliases()
# Target entropy is used when learning the entropy coefficient
if self.target_entropy == 'auto':
# automatically set target entropy if needed
self.target_entropy = -np.prod(self.env.action_space.shape).astype(
np.float32)
else:
# Force conversion
# this will also throw an error for unexpected string
self.target_entropy = float(self.target_entropy)
# The entropy coefficient or entropy can be learned automatically
# see Automating Entropy Adjustment for Maximum Entropy RL section
# of https://arxiv.org/abs/1812.05905
if isinstance(self.ent_coef, str) and self.ent_coef.startswith('auto'):
# Default initial value of ent_coef when learned
init_value = 1.0
if '_' in self.ent_coef:
init_value = float(self.ent_coef.split('_')[1])
assert init_value > 0., "The initial value of ent_coef must be greater than 0"
# Note: we optimize the log of the entropy coeff which is slightly different from the paper
# as discussed in https://github.com/rail-berkeley/softlearning/issues/37
self.log_ent_coef = th.log(
th.ones(1, device=self.device) *
init_value).requires_grad_(True)
self.ent_coef_optimizer = th.optim.Adam([self.log_ent_coef],
lr=self.lr_schedule(1))
else:
# Force conversion to float
# this will throw an error if a malformed string (different from 'auto')
# is passed
self.ent_coef_tensor = th.tensor(float(self.ent_coef)).to(
self.device)
self.bc_buffer = ReplayBuffer(int(1e4), self.observation_space,
self.action_space, self.device)
def _create_aliases(self) -> None:
self.actor = self.policy.actor
self.critic = self.policy.critic
self.critic_target = self.policy.critic_target
def pretrain_bc(self, gradient_steps: int, batch_size: int = 64):
statistics = []
with trange(gradient_steps) as t:
for gradient_step in t:
replay_data = self.bc_buffer.sample(
batch_size, env=self._vec_normalize_env)
dist = self.actor(replay_data.observations)
actions_pi, log_prob = dist.log_prob_and_rsample()
actor_loss = -log_prob.mean()
actor_mse_loss = F.mse_loss(actions_pi.detach(),
replay_data.actions)
self.actor.optimizer.zero_grad()
actor_loss.backward()
self.actor.optimizer.step()
statistics.append((actor_loss.item(), actor_mse_loss.item()))
t.set_postfix(mse_loss=actor_mse_loss.item(),
policy_loss=actor_loss.item())
actor_losses, mse_losses = tuple(zip(*statistics))
logger.record("pretrain/n_updates",
self._n_updates,
exclude='tensorboard')
logger.record("pretrain/actor_loss", np.mean(actor_losses))
logger.record("pretrain/actor_mse_loss", np.mean(mse_losses))
def pretrain_rl(self, gradient_steps: int, batch_size: int = 64) -> None:
statistics = []
with trange(gradient_steps) as t:
for gradient_step in t:
replay_data = self.replay_buffer.sample(
batch_size, env=self._vec_normalize_env)
stats = self.train_batch(replay_data)
statistics.append(stats)
self._n_updates += 1
t.set_postfix(qf_loss=stats[1], policy_loss=stats[0])
actor_losses, critic_losses, ent_coef_losses, ent_coefs = tuple(
zip(*statistics))
logger.record("pretrain/n_updates",
self._n_updates,
exclude='tensorboard')
logger.record("pretrain/ent_coef", np.mean(ent_coefs))
logger.record("pretrain/actor_loss", np.mean(actor_losses))
logger.record("pretrain/critic_loss", np.mean(critic_losses))
logger.record("pretrain/ent_coef_loss", np.mean(ent_coef_losses))
def train(self, gradient_steps: int, batch_size: int = 64) -> None:
statistics = []
for gradient_step in range(gradient_steps):
replay_data = self.replay_buffer.sample(
batch_size, env=self._vec_normalize_env)
stats = self.train_batch(replay_data)
statistics.append(stats)
self._n_updates += 1
actor_losses, critic_losses, ent_coef_losses, ent_coefs = tuple(
zip(*statistics))
logger.record("train/n_updates",
self._n_updates,
exclude='tensorboard')
logger.record("train/ent_coef", np.mean(ent_coefs))
logger.record("train/actor_loss", np.mean(actor_losses))
logger.record("train/critic_loss", np.mean(critic_losses))
logger.record("train/ent_coef_loss", np.mean(ent_coef_losses))
def train_batch(self, replay_data):
# Action by the current actor for the sampled state
dist = self.actor(replay_data.observations)
actions_pi, log_prob = dist.log_prob_and_rsample()
actor_mle = dist.mean
"""ent_coeff loss"""
ent_coef_loss = None
if self.ent_coef_optimizer is not None:
# Important: detach the variable from the graph
# so we don't change it with other losses
# see https://github.com/rail-berkeley/softlearning/issues/60
ent_coef = th.exp(self.log_ent_coef.detach())
ent_coef_loss = -(
self.log_ent_coef *
(log_prob + self.target_entropy).detach()).mean()
else:
ent_coef = self.ent_coef_tensor
"""q loss"""
with th.no_grad():
# Select action according to policy
next_dist = self.actor(replay_data.next_observations)
next_actions, next_log_prob = next_dist.log_prob_and_rsample()
# Compute the target Q value
target_q1, target_q2 = self.critic_target(
replay_data.next_observations, next_actions)
target_q = th.min(target_q1, target_q2)
target_q = replay_data.rewards + (
1 - replay_data.dones) * self.gamma * target_q
# td error + entropy term
# q_backup = target_q - ent_coef * next_log_prob
q_backup = target_q
# Get current Q estimates
# using action from the replay buffer
current_q1, current_q2 = self.critic(replay_data.observations,
replay_data.actions)
# Compute critic loss
critic_loss = 0.5 * (F.mse_loss(current_q1, q_backup) +
F.mse_loss(current_q2, q_backup))
"""action loss"""
# Advantage-weighted regression
# if self.awr_use_mle_for_vf:
# v1_pi,v2_pi = self.critic(replay_data.observations, actor_mle)
# v_pi = th.min(v1_pi, v2_pi)
# else:
# v1_pi,v2_pi = self.critic(replay_data.observations, actions_pi)
# v_pi = th.min(v1_pi, v2_pi)
q_adv = th.min(current_q1, current_q2)
v1_pi, v2_pi = self.critic(replay_data.observations, actor_mle)
v_pi = th.min(v1_pi, v2_pi)
# q_adv = th.min(*self.critic(replay_data.observations, actions_pi))
score = q_adv - v_pi
weights = F.softmax(score / self.beta, dim=0)
# actor_loss = ent_coef * log_prob.mean()
actor_logpp = dist.log_prob(replay_data.actions)
actor_loss = (-actor_logpp * len(weights) * weights.detach()).mean()
"""Updates"""
# Optimize entropy coefficient, also called
# entropy temperature or alpha in the paper
if ent_coef_loss is not None:
self.ent_coef_optimizer.zero_grad()
ent_coef_loss.backward()
self.ent_coef_optimizer.step()
# Optimize the critic
self.critic.optimizer.zero_grad()
critic_loss.backward()
self.critic.optimizer.step()
# Optimize the actor
self.actor.optimizer.zero_grad()
actor_loss.backward()
self.actor.optimizer.step()
# Update target networks
if self._n_updates % self.target_update_interval == 0:
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)
if ent_coef_loss is None:
ent_coef_loss = th.tensor([0])
return actor_loss.item(), critic_loss.item(), ent_coef_loss.item(
), ent_coef.item()
def learn(self,
total_timesteps: int,
callback: MaybeCallback = None,
log_interval: int = 4,
eval_env: Optional[GymEnv] = None,
eval_freq: int = -1,
n_eval_episodes: int = 5,
tb_log_name: str = "AWAC",
eval_log_path: Optional[str] = None,
reset_num_timesteps: bool = True) -> OffPolicyAlgorithm:
total_timesteps, callback = self._setup_learn(
total_timesteps, eval_env, callback, eval_freq, n_eval_episodes,
eval_log_path, reset_num_timesteps, tb_log_name)
callback.on_training_start(locals(), globals())
self.pretrain_bc(int(1e3), batch_size=self.batch_size)
observations, actions, next_observations, rewards, dones = self.bc_buffer.observations, self.bc_buffer.actions, self.bc_buffer.next_observations, self.bc_buffer.rewards, self.bc_buffer.dones
for data in zip(observations, next_observations, actions, rewards,
dones):
self.replay_buffer.add(*data)
self.pretrain_rl(int(1e4), batch_size=self.batch_size)
while self.num_timesteps < total_timesteps:
rollout = self.collect_rollouts(
self.env,
n_episodes=self.n_episodes_rollout,
n_steps=self.train_freq,
action_noise=self.action_noise,
callback=callback,
learning_starts=self.learning_starts,
replay_buffer=self.replay_buffer,
log_interval=log_interval)
if rollout.continue_training is False:
break
self._update_current_progress_remaining(self.num_timesteps,
total_timesteps)
if self.num_timesteps > 0 and self.num_timesteps > self.learning_starts:
gradient_steps = self.gradient_steps if self.gradient_steps > 0 else rollout.episode_timesteps
self.train(gradient_steps, batch_size=self.batch_size)
callback.on_training_end()
return self
def excluded_save_params(self) -> List[str]:
"""
Returns the names of the parameters that should be excluded by default
when saving the model.
:return: (List[str]) List of parameters that should be excluded from save
"""
# Exclude aliases
return super().excluded_save_params() + [
"actor", "critic", "critic_target"
]
def get_torch_variables(self) -> Tuple[List[str], List[str]]:
"""
cf base class
"""
state_dicts = ["policy", "actor.optimizer", "critic.optimizer"]
saved_tensors = ['log_ent_coef']
if self.ent_coef_optimizer is not None:
state_dicts.append('ent_coef_optimizer')
else:
saved_tensors.append('ent_coef_tensor')
return state_dicts, saved_tensors
class CMVCVaRSAC(OffPolicyAlgorithm):
def __init__(
self,
policy: Union[str, Type[CMVC51SACPolicy]],
env: Union[GymEnv, str],
min_v: float = -25,
max_v: float = +25,
support_dim: int = 200,
learning_rate: Union[float, Schedule] = 3e-4,
buffer_size: int = int(5e4),
learning_starts: int = 100,
batch_size: int = 64,
tau: float = 0.005,
gamma: float = 0.99,
train_freq: Union[int, Tuple[int, str]] = 1,
gradient_steps: int = 1,
action_noise: Optional[ActionNoise] = None,
optimize_memory_usage: bool = False,
ent_coef: Union[str, float] = "auto",
target_update_interval: int = 1,
target_entropy: Union[str, float] = "auto",
use_sde: bool = False,
sde_sample_freq: int = -1,
use_sde_at_warmup: bool = False,
tensorboard_log: Optional[str] = None,
create_eval_env: bool = False,
policy_kwargs: Dict[str, Any] = None,
verbose: int = 0,
seed: Optional[int] = None,
device: Union[th.device, str] = "auto",
_init_setup_model: bool = True,
cvar_alpha=0.3,
cmv_beta=1,
):
super(CMVCVaRSAC, self).__init__(
policy,
env,
CMVC51SACPolicy,
learning_rate,
buffer_size,
learning_starts,
batch_size,
tau,
gamma,
train_freq,
gradient_steps,
action_noise,
policy_kwargs=policy_kwargs,
tensorboard_log=tensorboard_log,
verbose=verbose,
device=device,
create_eval_env=create_eval_env,
seed=seed,
use_sde=use_sde,
sde_sample_freq=sde_sample_freq,
use_sde_at_warmup=use_sde_at_warmup,
optimize_memory_usage=optimize_memory_usage,
supported_action_spaces=(gym.spaces.Box),
)
self.target_entropy = target_entropy
self.log_ent_coef = None # type: Optional[th.Tensor]
# Entropy coefficient / Entropy temperature
# Inverse of the reward scale
self.ent_coef = ent_coef
self.target_update_interval = target_update_interval
self.ent_coef_optimizer = None
self.min_v = min_v
self.max_v = max_v
self.support_dim = support_dim
self.interval = (1 / (support_dim - 1)) * (max_v - min_v)
self.supports = th.from_numpy(
np.array([min_v + i * self.interval for i in range(support_dim)],
dtype=np.float32)).to(self.device)
self._total_timesteps = None
self.cvar_alpha = cvar_alpha
self.cmv_beta = cmv_beta
if _init_setup_model:
self._setup_model()
def _setup_model(self) -> None:
self._setup_lr_schedule()
self.set_random_seed(self.seed)
self.replay_buffer = ReplayBuffer(
self.buffer_size,
self.observation_space,
self.action_space,
self.device,
optimize_memory_usage=self.optimize_memory_usage,
)
self.policy = self.policy_class(
self.observation_space,
self.action_space,
self.support_dim,
self.lr_schedule,
**self.policy_kwargs, # pytype:disable=not-instantiable
)
self.policy = self.policy.to(self.device)
# Convert train freq parameter to TrainFreq object
self._convert_train_freq()
self._create_aliases()
# Target entropy is used when learning the entropy coefficient
if self.target_entropy == "auto":
# automatically set target entropy if needed
self.target_entropy = -np.prod(self.env.action_space.shape).astype(
np.float32)
else:
# Force conversion
# this will also throw an error for unexpected string
self.target_entropy = float(self.target_entropy)
# The entropy coefficient or entropy can be learned automatically
# see Automating Entropy Adjustment for Maximum Entropy RL section
# of https://arxiv.org/abs/1812.05905
if isinstance(self.ent_coef, str) and self.ent_coef.startswith("auto"):
# Default initial value of ent_coef when learned
init_value = 1.0
if "_" in self.ent_coef:
init_value = float(self.ent_coef.split("_")[1])
assert init_value > 0.0, "The initial value of ent_coef must be greater than 0"
# Note: we optimize the log of the entropy coeff which is slightly different from the paper
# as discussed in https://github.com/rail-berkeley/softlearning/issues/37
self.log_ent_coef = th.log(
th.ones(1, device=self.device) *
init_value).requires_grad_(True)
self.ent_coef_optimizer = th.optim.Adam([self.log_ent_coef],
lr=self.lr_schedule(1))
else:
# Force conversion to float
# this will throw an error if a malformed string (different from 'auto')
# is passed
self.ent_coef_tensor = th.tensor(float(self.ent_coef)).to(
self.device)
def _create_aliases(self) -> None:
self.actor = self.policy.actor
self.critic = self.policy.critic
self.critic_target = self.policy.critic_target
self.cmv_net = self.policy.cmv_net
self.beta_critic = self.policy.beta_critic
self.beta_critic_target = self.policy.beta_critic_target
def projection(self, support_rows, target_z):
projected_target_z = th.zeros_like(target_z)
support_rows = support_rows.clamp(self.min_v, self.max_v - 1e-3)
p = ((support_rows - self.min_v) % self.interval) / self.interval
idx = ((support_rows - self.min_v) // self.interval).long()
projected_target_z = projected_target_z.scatter_add(
1, idx, target_z * p)
projected_target_z = projected_target_z.scatter_add(
1, idx + 1, target_z * (1 - p))
return projected_target_z
def train(self, gradient_steps: int, batch_size: int = 64) -> None:
# Update optimizers learning rate
optimizers = [self.actor.optimizer, self.critic.optimizer]
if self.ent_coef_optimizer is not None:
optimizers += [self.ent_coef_optimizer]
# Update learning rate according to lr schedule
self._update_learning_rate(optimizers)
ent_coef_losses, ent_coefs = [], []
actor_losses, critic_losses, reward_losses, feature_pred_losses = [], [], [], []
cvars = []
qs = []
critic_beta_losses = []
for gradient_step in range(gradient_steps):
# Sample replay buffer
replay_data = self.replay_buffer.sample(
batch_size, env=self._vec_normalize_env)
# We need to sample because `log_std` may have changed between two gradient steps
if self.use_sde:
self.actor.reset_noise()
# Action by the current actor for the sampled state
actions_pi, log_prob = self.actor.action_log_prob(
replay_data.observations)
log_prob = log_prob.reshape(-1, 1)
ent_coef_loss = None
if self.ent_coef_optimizer is not None:
# Important: detach the variable from the graph
# so we don't change it with other losses
# see https://github.com/rail-berkeley/softlearning/issues/60
ent_coef = th.exp(self.log_ent_coef.detach())
ent_coef_loss = -(
self.log_ent_coef *
(log_prob + self.target_entropy).detach()).mean()
ent_coef_losses.append(ent_coef_loss.item())
else:
ent_coef = self.ent_coef_tensor
ent_coefs.append(ent_coef.item())
# Optimize entropy coefficient, also called
# entropy temperature or alpha in the paper
if ent_coef_loss is not None:
self.ent_coef_optimizer.zero_grad()
ent_coef_loss.backward()
self.ent_coef_optimizer.step()
with th.no_grad():
# Select action according to policy
next_actions, next_log_prob = self.actor.action_log_prob(
replay_data.next_observations)
# Compute the next Q values: min over all critics targets
target_zs = th.cat(self.critic_target(
replay_data.next_observations, next_actions),
dim=1)
# add entropy term
target_supports = self.supports.clone().detach()
target_supports = target_supports - ent_coef * next_log_prob.reshape(
-1, 1)
# td error + entropy term
target_supports = replay_data.rewards + (
1 - replay_data.dones) * self.gamma * target_supports
# Get current Q-values estimates for each critic network
# using action from the replay buffer
current_zs = th.cat(self.critic(replay_data.observations,
replay_data.actions),
dim=1)
target_zs = self.projection(target_supports, target_zs)
# Compute critic loss
critic_loss = -th.mean(th.log(current_zs + 1e-12) * target_zs)
critic_losses.append(critic_loss.item())
# Optimize the critic
self.critic.optimizer.zero_grad()
critic_loss.backward()
self.critic.optimizer.step()
# Compute
# For the CMV Learning
with th.no_grad():
next_z = self.critic_target.features_extractor(
replay_data.next_observations)
# predicted reward, and predicted next observation
r_pred, z_pred = self.cmv_net(replay_data.observations,
replay_data.actions)
mse_r_pred = th.mean(th.square(r_pred - replay_data.rewards))
mse_z_pred = th.mean(th.square(z_pred - next_z))
loss_cmv = mse_r_pred + mse_z_pred
reward_losses.append(mse_r_pred.item())
feature_pred_losses.append(mse_z_pred.item())
# Optimize the CMV Nets
self.cmv_net.optimizer.zero_grad()
loss_cmv.backward()
self.cmv_net.optimizer.step()
with th.no_grad():
# Select action according to policy
# Compute the next Q values: min over all critics targets
next_q_beta_values = th.cat(self.beta_critic_target(
replay_data.next_observations, next_actions),
dim=1)
next_q_beta_values, _ = th.min(next_q_beta_values,
dim=1,
keepdim=True)
# add entropy term
next_q_beta_values = next_q_beta_values - ent_coef * next_log_prob.reshape(
-1, 1)
# td error + entropy term
target_q_beta_values = loss_cmv.detach() + (
1 - replay_data.dones) * (self.gamma**
2) * next_q_beta_values
# Get current Q-beta values estimates for each critic network
# using action from the replay buffer
current_q_beta_values = self.beta_critic(replay_data.observations,
replay_data.actions)
# Compute critic beta loss
critic_beta_loss = \
0.5 * sum(
[F.mse_loss(current_q_beta, target_q_beta_values) for current_q_beta in current_q_beta_values])
critic_beta_losses.append(critic_beta_loss.item())
# Optimize the critic beta
self.beta_critic.optimizer.zero_grad()
critic_beta_loss.backward()
self.beta_critic.optimizer.step()
# Compute actor loss
# Alternative: actor_loss = th.mean(log_prob - qf1_pi - qf1_beta_pi)
# Mean over all critic networks
z_pi = th.cat(self.critic.forward(replay_data.observations,
actions_pi),
dim=1)
z_cdf = th.cumsum(z_pi, dim=-1)
adjust_pdf = th.where(th.le(z_cdf, self.cvar_alpha), z_pi,
th.zeros_like(z_pi))
adjust_pdf = th.div(adjust_pdf,
th.sum(adjust_pdf, dim=-1, keepdim=True))
q_pi = adjust_pdf @ self.supports
cvars.append(th.mean(q_pi).item())
qs.append(th.mean(z_pi @ self.supports).item())
q_beta_values_pi = th.cat(self.beta_critic.forward(
replay_data.observations, actions_pi),
dim=1)
max_qf_beta_pi, _ = th.max(q_beta_values_pi, dim=1, keepdim=True)
actor_loss = (ent_coef * log_prob - q_pi +
self.cmv_beta * next_q_beta_values).mean()
actor_losses.append(actor_loss.item())
# Optimize the actor
self.actor.optimizer.zero_grad()
actor_loss.backward()
self.actor.optimizer.step()
# Update target networks
if gradient_step % self.target_update_interval == 0:
polyak_update(self.critic.parameters(),
self.critic_target.parameters(), self.tau)
polyak_update(self.beta_critic.parameters(),
self.beta_critic_target.parameters(), self.tau)
self._n_updates += gradient_steps
fps = int(self.num_timesteps / (time.time() - self.start_time))
remaining_steps = self._total_timesteps - self.num_timesteps
eta = int(round(remaining_steps / fps))
logger.record("time/eta",
timedelta(seconds=eta),
exclude="tensorboard")
logger.record("train/CVaR Alpha", self.cvar_alpha)
logger.record("train/CMV Beta", self.cmv_beta)
logger.record("train/CVaR", np.mean(cvars))
logger.record("train/Q-value", np.mean(qs))
logger.record("train/n_updates",
self._n_updates,
exclude="tensorboard")
logger.record("train/ent_coef", np.mean(ent_coefs))
logger.record("train/actor_loss", np.mean(actor_losses))
logger.record("train/critic_loss", np.mean(critic_losses))
logger.record("train/reward error", np.mean(reward_losses))
logger.record("train/s_t+1_error", np.mean(feature_pred_losses))
logger.record("train/beta_Q_loss", np.mean(critic_beta_losses))
if len(ent_coef_losses) > 0:
logger.record("train/ent_coef_loss", np.mean(ent_coef_losses))
def learn(
self,
total_timesteps: int,
callback: MaybeCallback = None,
log_interval: int = 4,
eval_env: Optional[GymEnv] = None,
eval_freq: int = -1,
n_eval_episodes: int = 5,
tb_log_name: str = "C51SAC",
eval_log_path: Optional[str] = None,
reset_num_timesteps: bool = True,
) -> OffPolicyAlgorithm:
return super(CMVCVaRSAC, self).learn(
total_timesteps=total_timesteps,
callback=callback,
log_interval=log_interval,
eval_env=eval_env,
eval_freq=eval_freq,
n_eval_episodes=n_eval_episodes,
tb_log_name=tb_log_name,
eval_log_path=eval_log_path,
reset_num_timesteps=reset_num_timesteps,
)
def _excluded_save_params(self) -> List[str]:
return super(CMVCVaRSAC, self)._excluded_save_params() + [
"actor", "critic", "critic_target"
]
def _get_torch_save_params(self) -> Tuple[List[str], List[str]]:
state_dicts = ["policy", "actor.optimizer", "critic.optimizer"]
saved_pytorch_variables = ["log_ent_coef"]
if self.ent_coef_optimizer is not None:
state_dicts.append("ent_coef_optimizer")
else:
saved_pytorch_variables.append("ent_coef_tensor")
return state_dicts, saved_pytorch_variables
