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import gym
import torch
import torch.nn as nn
from typing import Sequence, Type
from shared.module.feature_extractor import FeatureExtractor
from shared.module.module import mlp
class CriticHead(nn.Module):
def __init__(
self,
hidden_sizes: Sequence[int] = (32,),
activation: Type[nn.Module] = nn.Tanh,
init_layers_orthogonal: bool = True,
) -> None:
super().__init__()
layer_sizes = tuple(hidden_sizes) + (1,)
self._fc = mlp(
layer_sizes,
activation,
init_layers_orthogonal=init_layers_orthogonal,
final_layer_gain=1.0,
)
def forward(self, obs: torch.Tensor) -> torch.Tensor:
v = self._fc(obs)
return v.squeeze(-1)
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