33 lines
895 B
Python
33 lines
895 B
Python
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import torch
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import torch.nn as nn
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import numpy as np
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class DQN(nn.Module):
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def __init__(self, input_shape, n_actions):
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super(DQN, self).__init__()
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self.conv = nn.Sequential(
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nn.Conv2d(input_shape[0], 32, kernel_size=8, stride=4),
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nn.ReLU(),
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nn.Conv2d(32, 64, kernel_size=4, stride=2),
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nn.ReLU(),
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nn.Conv2d(64, 64, kernel_size=3, stride=1),
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nn.ReLU()
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)
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conv_out_size = self._get_conv_out(input_shape)
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self.fc = nn.Sequential(
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nn.Linear(conv_out_size, 512),
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nn.ReLU(),
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nn.Linear(512, n_actions)
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)
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def _get_conv_out(self, shape):
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o = self.conv(torch.zeros(1, *shape))
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return int(np.prod(o.size()))
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def forward(self, x):
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conv_out = self.conv(x).view(x.size()[0], -1)
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return self.fc(conv_out)
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