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from typing import Optional, Tuple, Type
import torch
import torch.nn as nn
from torch.distributions import Categorical
from rl_algo_impls.shared.actor import Actor, PiForward, pi_forward
from rl_algo_impls.shared.module.utils import mlp
class MaskedCategorical(Categorical):
def __init__(
self,
probs=None,
logits=None,
validate_args=None,
mask: Optional[torch.Tensor] = None,
):
if mask is not None:
assert logits is not None, "mask requires logits and not probs"
logits = torch.where(mask, logits, -1e8)
self.mask = mask
super().__init__(probs, logits, validate_args)
def entropy(self) -> torch.Tensor:
if self.mask is None:
return super().entropy()
# If mask set, then use approximation for entropy
p_log_p = self.logits * self.probs # type: ignore
masked = torch.where(self.mask, p_log_p, 0)
return -masked.sum(-1)
class CategoricalActorHead(Actor):
def __init__(
self,
act_dim: int,
in_dim: int,
hidden_sizes: Tuple[int, ...] = (32,),
activation: Type[nn.Module] = nn.Tanh,
init_layers_orthogonal: bool = True,
) -> None:
super().__init__()
layer_sizes = (in_dim,) + hidden_sizes + (act_dim,)
self._fc = mlp(
layer_sizes,
activation,
init_layers_orthogonal=init_layers_orthogonal,
final_layer_gain=0.01,
)
def forward(
self,
obs: torch.Tensor,
actions: Optional[torch.Tensor] = None,
action_masks: Optional[torch.Tensor] = None,
) -> PiForward:
logits = self._fc(obs)
pi = MaskedCategorical(logits=logits, mask=action_masks)
return pi_forward(pi, actions)
@property
def action_shape(self) -> Tuple[int, ...]:
return ()
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