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from typing import Optional, Sequence, Type
import gym
import torch as th
import torch.nn as nn
from gym.spaces import Discrete
from rl_algo_impls.shared.encoder import Encoder
from rl_algo_impls.shared.module.utils import mlp
class QNetwork(nn.Module):
def __init__(
self,
observation_space: gym.Space,
action_space: gym.Space,
hidden_sizes: Sequence[int] = [],
activation: Type[nn.Module] = nn.ReLU, # Used by stable-baselines3
cnn_flatten_dim: int = 512,
cnn_style: str = "nature",
cnn_layers_init_orthogonal: Optional[bool] = None,
impala_channels: Sequence[int] = (16, 32, 32),
) -> None:
super().__init__()
assert isinstance(action_space, Discrete)
self._feature_extractor = Encoder(
observation_space,
activation,
cnn_flatten_dim=cnn_flatten_dim,
cnn_style=cnn_style,
cnn_layers_init_orthogonal=cnn_layers_init_orthogonal,
impala_channels=impala_channels,
)
layer_sizes = (
(self._feature_extractor.out_dim,) + tuple(hidden_sizes) + (action_space.n,)
)
self._fc = mlp(layer_sizes, activation)
def forward(self, obs: th.Tensor) -> th.Tensor:
x = self._feature_extractor(obs)
return self._fc(x)
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