mirror of
https://github.com/saymrwulf/stable-baselines3.git
synced 2026-09-05 20:30:42 +00:00
208 lines
10 KiB
Python
208 lines
10 KiB
Python
from typing import Optional, List, Tuple, Callable, Union, Dict
|
|
from functools import partial
|
|
|
|
import gym
|
|
import torch as th
|
|
import torch.nn as nn
|
|
import numpy as np
|
|
|
|
from torchy_baselines.common.policies import (BasePolicy, register_policy, MlpExtractor,
|
|
create_sde_feature_extractor)
|
|
from torchy_baselines.common.distributions import (make_proba_distribution, Distribution,
|
|
DiagGaussianDistribution, CategoricalDistribution,
|
|
StateDependentNoiseDistribution)
|
|
|
|
|
|
class PPOPolicy(BasePolicy):
|
|
"""
|
|
Policy class (with both actor and critic) for A2C and derivates (PPO).
|
|
|
|
:param observation_space: (gym.spaces.Space) Observation space
|
|
:param action_space: (gym.spaces.Space) Action space
|
|
:param lr_schedule: (Callable) Learning rate schedule (could be constant)
|
|
:param net_arch: ([int or dict]) The specification of the policy and value networks.
|
|
:param device: (str or th.device) Device on which the code should run.
|
|
:param activation_fn: (nn.Module) Activation function
|
|
:param adam_epsilon: (float) Small values to avoid NaN in ADAM optimizer
|
|
:param ortho_init: (bool) Whether to use or not orthogonal initialization
|
|
:param use_sde: (bool) Whether to use State Dependent Exploration or not
|
|
:param log_std_init: (float) Initial value for the log standard deviation
|
|
:param full_std: (bool) Whether to use (n_features x n_actions) parameters
|
|
for the std instead of only (n_features,) when using SDE
|
|
:param sde_net_arch: ([int]) Network architecture for extracting features
|
|
when using SDE. If None, the latent features from the policy will be used.
|
|
Pass an empty list to use the states as features.
|
|
:param use_expln: (bool) Use ``expln()`` function instead of ``exp()`` to ensure
|
|
a positive standard deviation (cf paper). It allows to keep variance
|
|
above zero and prevent it from growing too fast. In practice, ``exp()`` is usually enough.
|
|
:param squash_output: (bool) Whether to squash the output using a tanh function,
|
|
this allows to ensure boundaries when using SDE.
|
|
"""
|
|
def __init__(self,
|
|
observation_space: gym.spaces.Space,
|
|
action_space: gym.spaces.Space,
|
|
lr_schedule: Callable,
|
|
net_arch: Optional[List[Union[int, Dict[str, List[int]]]]] = None,
|
|
device: Union[th.device, str] = 'cpu',
|
|
activation_fn: nn.Module = nn.Tanh,
|
|
adam_epsilon: float = 1e-5,
|
|
ortho_init: bool = True,
|
|
use_sde: bool = False,
|
|
log_std_init: float = 0.0,
|
|
full_std: bool = True,
|
|
sde_net_arch: Optional[List[int]] = None,
|
|
use_expln: bool = False,
|
|
squash_output: bool = False):
|
|
super(PPOPolicy, self).__init__(observation_space, action_space, device, squash_output=squash_output)
|
|
self.obs_dim = self.observation_space.shape[0]
|
|
|
|
# Default network architecture, from stable-baselines
|
|
if net_arch is None:
|
|
net_arch = [dict(pi=[64, 64], vf=[64, 64])]
|
|
|
|
self.net_arch = net_arch
|
|
self.activation_fn = activation_fn
|
|
self.adam_epsilon = adam_epsilon
|
|
self.ortho_init = ortho_init
|
|
self.net_args = {
|
|
'input_dim': self.obs_dim,
|
|
'output_dim': -1,
|
|
'net_arch': self.net_arch,
|
|
'activation_fn': self.activation_fn
|
|
}
|
|
self.shared_net = None
|
|
self.pi_net, self.vf_net = None, None
|
|
# In the future, feature_extractor will be replaced with a CNN
|
|
self.features_extractor = nn.Flatten()
|
|
self.features_dim = self.obs_dim
|
|
self.log_std_init = log_std_init
|
|
dist_kwargs = None
|
|
# Keyword arguments for SDE distribution
|
|
if use_sde:
|
|
dist_kwargs = {
|
|
'full_std': full_std,
|
|
'squash_output': squash_output,
|
|
'use_expln': use_expln,
|
|
'learn_features': sde_net_arch is not None
|
|
}
|
|
|
|
self.sde_feature_extractor = None
|
|
self.sde_net_arch = sde_net_arch
|
|
self.use_sde = use_sde
|
|
|
|
# Action distribution
|
|
self.action_dist = make_proba_distribution(action_space, use_sde=use_sde, dist_kwargs=dist_kwargs)
|
|
|
|
self._build(lr_schedule)
|
|
|
|
def reset_noise(self, n_envs: int = 1) -> None:
|
|
"""
|
|
Sample new weights for the exploration matrix.
|
|
|
|
:param n_envs: (int)
|
|
"""
|
|
assert isinstance(self.action_dist, StateDependentNoiseDistribution), 'reset_noise() is only available when using SDE'
|
|
self.action_dist.sample_weights(self.log_std, batch_size=n_envs)
|
|
|
|
def _build(self, lr_schedule: Callable) -> None:
|
|
self.mlp_extractor = MlpExtractor(self.features_dim, net_arch=self.net_arch,
|
|
activation_fn=self.activation_fn, device=self.device)
|
|
|
|
latent_dim_pi = self.mlp_extractor.latent_dim_pi
|
|
|
|
# Separate feature extractor for SDE
|
|
if self.sde_net_arch is not None:
|
|
self.sde_feature_extractor, latent_sde_dim = create_sde_feature_extractor(self.features_dim,
|
|
self.sde_net_arch,
|
|
self.activation_fn)
|
|
|
|
if isinstance(self.action_dist, DiagGaussianDistribution):
|
|
self.action_net, self.log_std = self.action_dist.proba_distribution_net(latent_dim=latent_dim_pi,
|
|
log_std_init=self.log_std_init)
|
|
elif isinstance(self.action_dist, StateDependentNoiseDistribution):
|
|
latent_sde_dim = latent_dim_pi if self.sde_net_arch is None else latent_sde_dim
|
|
self.action_net, self.log_std = self.action_dist.proba_distribution_net(latent_dim=latent_dim_pi,
|
|
latent_sde_dim=latent_sde_dim,
|
|
log_std_init=self.log_std_init)
|
|
elif isinstance(self.action_dist, CategoricalDistribution):
|
|
self.action_net = self.action_dist.proba_distribution_net(latent_dim=latent_dim_pi)
|
|
|
|
self.value_net = nn.Linear(self.mlp_extractor.latent_dim_vf, 1)
|
|
# Init weights: use orthogonal initialization
|
|
# with small initial weight for the output
|
|
if self.ortho_init:
|
|
for module in [self.mlp_extractor, self.action_net, self.value_net]:
|
|
# Values from stable-baselines, TODO: check why
|
|
gain = {
|
|
self.mlp_extractor: np.sqrt(2),
|
|
self.action_net: 0.01,
|
|
self.value_net: 1
|
|
}[module]
|
|
module.apply(partial(self.init_weights, gain=gain))
|
|
self.optimizer = th.optim.Adam(self.parameters(), lr=lr_schedule(1), eps=self.adam_epsilon)
|
|
|
|
def forward(self, obs: th.Tensor, deterministic: bool = False) -> Tuple[th.Tensor, th.Tensor, th.Tensor]:
|
|
if not isinstance(obs, th.Tensor):
|
|
obs = th.FloatTensor(obs).to(self.device)
|
|
latent_pi, latent_vf, latent_sde = self._get_latent(obs)
|
|
value = self.value_net(latent_vf)
|
|
action, action_distribution = self._get_action_dist_from_latent(latent_pi, latent_sde=latent_sde,
|
|
deterministic=deterministic)
|
|
log_prob = action_distribution.log_prob(action)
|
|
return action, value, log_prob
|
|
|
|
def _get_latent(self, obs: th.Tensor) -> Tuple[th.Tensor, th.Tensor, th.Tensor]:
|
|
features = self.features_extractor(obs)
|
|
latent_pi, latent_vf = self.mlp_extractor(features)
|
|
# Features for sde
|
|
latent_sde = latent_pi
|
|
if self.sde_feature_extractor is not None:
|
|
latent_sde = self.sde_feature_extractor(features)
|
|
return latent_pi, latent_vf, latent_sde
|
|
|
|
def _get_action_dist_from_latent(self, latent_pi: th.Tensor,
|
|
latent_sde: Optional[th.Tensor] = None,
|
|
deterministic: bool = False) -> Tuple[th.Tensor, Distribution]:
|
|
mean_actions = self.action_net(latent_pi)
|
|
|
|
if isinstance(self.action_dist, DiagGaussianDistribution):
|
|
return self.action_dist.proba_distribution(mean_actions, self.log_std, deterministic=deterministic)
|
|
|
|
elif isinstance(self.action_dist, CategoricalDistribution):
|
|
# Here mean_actions are the logits before the softmax
|
|
return self.action_dist.proba_distribution(mean_actions, deterministic=deterministic)
|
|
|
|
elif isinstance(self.action_dist, StateDependentNoiseDistribution):
|
|
return self.action_dist.proba_distribution(mean_actions, self.log_std, latent_sde,
|
|
deterministic=deterministic)
|
|
else:
|
|
raise ValueError('Invalid action distribution')
|
|
|
|
def predict(self, observation: th.Tensor, deterministic: bool = False) -> th.Tensor:
|
|
latent_pi, _, latent_sde = self._get_latent(observation)
|
|
action, _ = self._get_action_dist_from_latent(latent_pi, latent_sde, deterministic=deterministic)
|
|
return action
|
|
|
|
def evaluate_actions(self, obs: th.Tensor,
|
|
actions: th.Tensor,
|
|
deterministic: bool = False) -> Tuple[th.Tensor, th.Tensor, th.Tensor]:
|
|
"""
|
|
Evaluate actions according to the current policy,
|
|
given the observations.
|
|
|
|
:param obs: (th.Tensor)
|
|
:param actions: (th.Tensor)
|
|
:param deterministic: (bool)
|
|
:return: (th.Tensor, th.Tensor, th.Tensor) estimated value, log likelihood of taking those actions
|
|
and entropy of the action distribution.
|
|
"""
|
|
latent_pi, latent_vf, latent_sde = self._get_latent(obs)
|
|
_, action_distribution = self._get_action_dist_from_latent(latent_pi, latent_sde, deterministic=deterministic)
|
|
log_prob = action_distribution.log_prob(actions)
|
|
values = self.value_net(latent_vf)
|
|
return values, log_prob, action_distribution.entropy()
|
|
|
|
|
|
MlpPolicy = PPOPolicy
|
|
|
|
register_policy("MlpPolicy", MlpPolicy)
|