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Smashing bugs: dont confuse chol with chol_net

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This commit is contained in:
Dominik Moritz Roth 2022-07-19 10:07:20 +02:00
parent b7de99b1fc
commit 5f32435751
1 changed files with 57 additions and 28 deletions
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85
metastable_baselines/sac/policies.py
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@ -1,6 +1,8 @@
import warnings
from typing import Any, Dict, List, Optional, Tuple, Type, Union
import math
import gym
import torch as th
from torch import nn
@ -18,6 +20,8 @@ from stable_baselines3.common.torch_layers import (
)
from stable_baselines3.common.type_aliases import Schedule
from ..distributions import UniversalGaussianDistribution
# CAP the standard deviation of the actor
LOG_STD_MAX = 2
LOG_STD_MIN = -20
@ -64,6 +68,7 @@ class Actor(BasePolicy):
use_expln: bool = False,
clip_mean: float = 2.0,
normalize_images: bool = True,
dist_kwargs={},
):
super().__init__(
observation_space,
@ -86,7 +91,8 @@ class Actor(BasePolicy):
self.clip_mean = clip_mean
if sde_net_arch is not None:
warnings.warn("sde_net_arch is deprecated and will be removed in SB3 v2.4.0.", DeprecationWarning)
warnings.warn(
"sde_net_arch is deprecated and will be removed in SB3 v2.4.0.", DeprecationWarning)
action_dim = get_action_dim(self.action_space)
latent_pi_net = create_mlp(features_dim, -1, net_arch, activation_fn)
@ -94,20 +100,29 @@ class Actor(BasePolicy):
last_layer_dim = net_arch[-1] if len(net_arch) > 0 else features_dim
if self.use_sde:
# TODO: Port to UGD
self.action_dist = StateDependentNoiseDistribution(
action_dim, full_std=full_std, use_expln=use_expln, learn_features=True, squash_output=True
)
self.mu, self.log_std = self.action_dist.proba_distribution_net(
self.mu_net, self.chol_net = self.action_dist.proba_distribution_net(
latent_dim=last_layer_dim, latent_sde_dim=last_layer_dim, log_std_init=log_std_init
)
# Avoid numerical issues by limiting the mean of the Gaussian
# to be in [-clip_mean, clip_mean]
if clip_mean > 0.0:
self.mu = nn.Sequential(self.mu, nn.Hardtanh(min_val=-clip_mean, max_val=clip_mean))
self.mu = nn.Sequential(self.mu, nn.Hardtanh(
min_val=-clip_mean, max_val=clip_mean))
else:
self.action_dist = SquashedDiagGaussianDistribution(action_dim)
self.mu = nn.Linear(last_layer_dim, action_dim)
self.log_std = nn.Linear(last_layer_dim, action_dim)
self.action_dist = UniversalGaussianDistribution(
action_dim, **dist_kwargs)
self.mu_net, self.chol_net = self.action_dist.proba_distribution_net(
latent_dim=last_layer_dim, latent_sde_dim=last_layer_dim, std_init=math.exp(
self.log_std_init)
)
#self.action_dist = SquashedDiagGaussianDistribution(action_dim)
#self.mu = nn.Linear(last_layer_dim, action_dim)
#self.log_std = nn.Linear(last_layer_dim, action_dim)
def _get_constructor_parameters(self) -> Dict[str, Any]:
data = super()._get_constructor_parameters()
@ -138,8 +153,9 @@ class Actor(BasePolicy):
:return:
"""
msg = "get_std() is only available when using gSDE"
assert isinstance(self.action_dist, StateDependentNoiseDistribution), msg
return self.action_dist.get_std(self.log_std)
assert isinstance(self.action_dist,
StateDependentNoiseDistribution), msg
return self.chol
def reset_noise(self, batch_size: int = 1) -> None:
"""
@ -148,8 +164,9 @@ class Actor(BasePolicy):
:param batch_size:
"""
msg = "reset_noise() is only available when using gSDE"
assert isinstance(self.action_dist, StateDependentNoiseDistribution), msg
self.action_dist.sample_weights(self.log_std, batch_size=batch_size)
assert isinstance(self.action_dist,
StateDependentNoiseDistribution), msg
self.action_dist.sample_weights(self.chol, batch_size=batch_size)
def get_action_dist_params(self, obs: th.Tensor) -> Tuple[th.Tensor, th.Tensor, Dict[str, th.Tensor]]:
"""
@ -161,25 +178,25 @@ class Actor(BasePolicy):
"""
features = self.extract_features(obs)
latent_pi = self.latent_pi(features)
mean_actions = self.mu(latent_pi)
mean_actions = self.mu_net(latent_pi)
if self.use_sde:
return mean_actions, self.log_std, dict(latent_sde=latent_pi)
return mean_actions, self.chol, dict(latent_sde=latent_pi)
# Unstructured exploration (Original implementation)
log_std = self.log_std(latent_pi)
chol = self.chol_net(latent_pi)
# Original Implementation to cap the standard deviation
log_std = th.clamp(log_std, LOG_STD_MIN, LOG_STD_MAX)
return mean_actions, log_std, {}
self.chol = th.clamp(chol, LOG_STD_MIN, LOG_STD_MAX)
return mean_actions, self.chol, {}
def forward(self, obs: th.Tensor, deterministic: bool = False) -> th.Tensor:
mean_actions, log_std, kwargs = self.get_action_dist_params(obs)
mean_actions, chol, kwargs = self.get_action_dist_params(obs)
# Note: the action is squashed
return self.action_dist.actions_from_params(mean_actions, log_std, deterministic=deterministic, **kwargs)
return self.action_dist.actions_from_params(mean_actions, chol, deterministic=deterministic, **kwargs)
def action_log_prob(self, obs: th.Tensor) -> Tuple[th.Tensor, th.Tensor]:
mean_actions, log_std, kwargs = self.get_action_dist_params(obs)
mean_actions, chol, kwargs = self.get_action_dist_params(obs)
# return action and associated log prob
return self.action_dist.log_prob_from_params(mean_actions, log_std, **kwargs)
return self.action_dist.log_prob_from_params(mean_actions, chol, **kwargs)
def _predict(self, observation: th.Tensor, deterministic: bool = False) -> th.Tensor:
return self(observation, deterministic)
@ -236,6 +253,7 @@ class SACPolicy(BasePolicy):
optimizer_kwargs: Optional[Dict[str, Any]] = None,
n_critics: int = 2,
share_features_extractor: bool = True,
dist_kwargs={},
):
super().__init__(
observation_space,
@ -267,7 +285,8 @@ class SACPolicy(BasePolicy):
self.actor_kwargs = self.net_args.copy()
if sde_net_arch is not None:
warnings.warn("sde_net_arch is deprecated and will be removed in SB3 v2.4.0.", DeprecationWarning)
warnings.warn(
"sde_net_arch is deprecated and will be removed in SB3 v2.4.0.", DeprecationWarning)
sde_kwargs = {
"use_sde": use_sde,
@ -275,6 +294,7 @@ class SACPolicy(BasePolicy):
"use_expln": use_expln,
"clip_mean": clip_mean,
}
self.actor_kwargs.update(sde_kwargs)
self.critic_kwargs = self.net_args.copy()
self.critic_kwargs.update(
@ -289,17 +309,22 @@ class SACPolicy(BasePolicy):
self.critic, self.critic_target = None, None
self.share_features_extractor = share_features_extractor
self.dist_kwargs = dist_kwargs
self._build(lr_schedule)
def _build(self, lr_schedule: Schedule) -> None:
self.actor = self.make_actor()
self.actor.optimizer = self.optimizer_class(self.actor.parameters(), lr=lr_schedule(1), **self.optimizer_kwargs)
self.actor.optimizer = self.optimizer_class(
self.actor.parameters(), lr=lr_schedule(1), **self.optimizer_kwargs)
if self.share_features_extractor:
self.critic = self.make_critic(features_extractor=self.actor.features_extractor)
self.critic = self.make_critic(
features_extractor=self.actor.features_extractor)
# Do not optimize the shared features extractor with the critic loss
# otherwise, there are gradient computation issues
critic_parameters = [param for name, param in self.critic.named_parameters() if "features_extractor" not in name]
critic_parameters = [param for name, param in self.critic.named_parameters(
) if "features_extractor" not in name]
else:
# Create a separate features extractor for the critic
# this requires more memory and computation
@ -310,7 +335,8 @@ class SACPolicy(BasePolicy):
self.critic_target = self.make_critic(features_extractor=None)
self.critic_target.load_state_dict(self.critic.state_dict())
self.critic.optimizer = self.optimizer_class(critic_parameters, lr=lr_schedule(1), **self.optimizer_kwargs)
self.critic.optimizer = self.optimizer_class(
critic_parameters, lr=lr_schedule(1), **self.optimizer_kwargs)
# Target networks should always be in eval mode
self.critic_target.set_training_mode(False)
@ -327,7 +353,8 @@ class SACPolicy(BasePolicy):
use_expln=self.actor_kwargs["use_expln"],
clip_mean=self.actor_kwargs["clip_mean"],
n_critics=self.critic_kwargs["n_critics"],
lr_schedule=self._dummy_schedule, # dummy lr schedule, not needed for loading policy alone
# dummy lr schedule, not needed for loading policy alone
lr_schedule=self._dummy_schedule,
optimizer_class=self.optimizer_class,
optimizer_kwargs=self.optimizer_kwargs,
features_extractor_class=self.features_extractor_class,
@ -345,11 +372,13 @@ class SACPolicy(BasePolicy):
self.actor.reset_noise(batch_size=batch_size)
def make_actor(self, features_extractor: Optional[BaseFeaturesExtractor] = None) -> Actor:
actor_kwargs = self._update_features_extractor(self.actor_kwargs, features_extractor)
return Actor(**actor_kwargs).to(self.device)
actor_kwargs = self._update_features_extractor(
self.actor_kwargs, features_extractor)
return Actor(**actor_kwargs, dist_kwargs=self.dist_kwargs).to(self.device)
def make_critic(self, features_extractor: Optional[BaseFeaturesExtractor] = None) -> ContinuousCritic:
critic_kwargs = self._update_features_extractor(self.critic_kwargs, features_extractor)
critic_kwargs = self._update_features_extractor(
self.critic_kwargs, features_extractor)
return ContinuousCritic(**critic_kwargs).to(self.device)
def forward(self, obs: th.Tensor, deterministic: bool = False) -> th.Tensor: