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from typing import Optional, Tuple, Type, Union
import gym
import torch
import torch.nn as nn
from rl_algo_impls.shared.encoder.cnn import CnnEncoder, EncoderOutDim
from rl_algo_impls.shared.module.utils import layer_init
class GridnetEncoder(CnnEncoder):
"""
Encoder for encoder-decoder for Gym-MicroRTS
"""
def __init__(
self,
obs_space: gym.Space,
activation: Type[nn.Module] = nn.ReLU,
cnn_init_layers_orthogonal: Optional[bool] = None,
**kwargs
) -> None:
if cnn_init_layers_orthogonal is None:
cnn_init_layers_orthogonal = True
super().__init__(obs_space, **kwargs)
in_channels = obs_space.shape[0] # type: ignore
self.encoder = nn.Sequential(
layer_init(
nn.Conv2d(in_channels, 32, kernel_size=3, padding=1),
cnn_init_layers_orthogonal,
),
nn.MaxPool2d(3, stride=2, padding=1),
activation(),
layer_init(
nn.Conv2d(32, 64, kernel_size=3, padding=1),
cnn_init_layers_orthogonal,
),
nn.MaxPool2d(3, stride=2, padding=1),
activation(),
layer_init(
nn.Conv2d(64, 128, kernel_size=3, padding=1),
cnn_init_layers_orthogonal,
),
nn.MaxPool2d(3, stride=2, padding=1),
activation(),
layer_init(
nn.Conv2d(128, 256, kernel_size=3, padding=1),
cnn_init_layers_orthogonal,
),
nn.MaxPool2d(3, stride=2, padding=1),
activation(),
)
with torch.no_grad():
encoder_out = self.encoder(
self.preprocess(torch.as_tensor(obs_space.sample())) # type: ignore
)
self._out_dim = encoder_out.shape[1:]
def forward(self, obs: torch.Tensor) -> torch.Tensor:
return self.encoder(super().forward(obs))
@property
def out_dim(self) -> EncoderOutDim:
return self._out_dim
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