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from typing import Optional, Sequence, Tuple
import torch
import torch.nn as nn
from gym.spaces import Space
from rl_algo_impls.shared.actor import actor_head
from rl_algo_impls.shared.encoder import Encoder
from rl_algo_impls.shared.policy.actor_critic_network.network import (
ACNForward,
ActorCriticNetwork,
default_hidden_sizes,
)
from rl_algo_impls.shared.policy.critic import CriticHead
from rl_algo_impls.shared.policy.policy import ACTIVATION
class SeparateActorCriticNetwork(ActorCriticNetwork):
def __init__(
self,
observation_space: Space,
action_space: Space,
pi_hidden_sizes: Optional[Sequence[int]] = None,
v_hidden_sizes: Optional[Sequence[int]] = None,
init_layers_orthogonal: bool = True,
activation_fn: str = "tanh",
log_std_init: float = -0.5,
use_sde: bool = False,
full_std: bool = True,
squash_output: bool = False,
cnn_flatten_dim: int = 512,
cnn_style: str = "nature",
cnn_layers_init_orthogonal: Optional[bool] = None,
impala_channels: Sequence[int] = (16, 32, 32),
actor_head_style: str = "single",
action_plane_space: Optional[Space] = None,
) -> None:
super().__init__()
pi_hidden_sizes = (
pi_hidden_sizes
if pi_hidden_sizes is not None
else default_hidden_sizes(observation_space)
)
v_hidden_sizes = (
v_hidden_sizes
if v_hidden_sizes is not None
else default_hidden_sizes(observation_space)
)
activation = ACTIVATION[activation_fn]
self._feature_extractor = Encoder(
observation_space,
activation,
init_layers_orthogonal=init_layers_orthogonal,
cnn_flatten_dim=cnn_flatten_dim,
cnn_style=cnn_style,
cnn_layers_init_orthogonal=cnn_layers_init_orthogonal,
impala_channels=impala_channels,
)
self._pi = actor_head(
action_space,
self._feature_extractor.out_dim,
tuple(pi_hidden_sizes),
init_layers_orthogonal,
activation,
log_std_init=log_std_init,
use_sde=use_sde,
full_std=full_std,
squash_output=squash_output,
actor_head_style=actor_head_style,
action_plane_space=action_plane_space,
)
v_encoder = Encoder(
observation_space,
activation,
init_layers_orthogonal=init_layers_orthogonal,
cnn_flatten_dim=cnn_flatten_dim,
cnn_style=cnn_style,
cnn_layers_init_orthogonal=cnn_layers_init_orthogonal,
)
self._v = nn.Sequential(
v_encoder,
CriticHead(
in_dim=v_encoder.out_dim,
hidden_sizes=v_hidden_sizes,
activation=activation,
init_layers_orthogonal=init_layers_orthogonal,
),
)
def forward(
self,
obs: torch.Tensor,
action: torch.Tensor,
action_masks: Optional[torch.Tensor] = None,
) -> ACNForward:
return self._distribution_and_value(
obs, action=action, action_masks=action_masks
)
def distribution_and_value(
self, obs: torch.Tensor, action_masks: Optional[torch.Tensor] = None
) -> ACNForward:
return self._distribution_and_value(obs, action_masks=action_masks)
def _distribution_and_value(
self,
obs: torch.Tensor,
action: Optional[torch.Tensor] = None,
action_masks: Optional[torch.Tensor] = None,
) -> ACNForward:
pi_forward = self._pi(
self._feature_extractor(obs), actions=action, action_masks=action_masks
)
v = self._v(obs)
return ACNForward(pi_forward, v)
def value(self, obs: torch.Tensor) -> torch.Tensor:
return self._v(obs)
def reset_noise(self, batch_size: int) -> None:
self._pi.sample_weights(batch_size=batch_size)
@property
def action_shape(self) -> Tuple[int, ...]:
return self._pi.action_shape
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