e767de044b
* remove dummy net; delete two files * split code to have backbone and head * rename class * change torch.float to torch.float32 * use flatten(1) instead of view(batch, -1) * remove dummy net in docs * bugfix for rnn * fix cuda error * minor fix of docs * do not change the example code in dqn tutorial, since it is for demonstration Co-authored-by: Trinkle23897 <463003665@qq.com>
73 lines
2.4 KiB
Python
73 lines
2.4 KiB
Python
import torch
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import numpy as np
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from torch import nn
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import torch.nn.functional as F
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class Actor(nn.Module):
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"""For advanced usage (how to customize the network), please refer to
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:ref:`build_the_network`.
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"""
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def __init__(self, preprocess_net, action_shape):
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super().__init__()
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self.preprocess = preprocess_net
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self.last = nn.Linear(128, np.prod(action_shape))
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def forward(self, s, state=None, info={}):
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logits, h = self.preprocess(s, state)
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logits = F.softmax(self.last(logits), dim=-1)
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return logits, h
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class Critic(nn.Module):
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"""For advanced usage (how to customize the network), please refer to
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:ref:`build_the_network`.
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"""
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def __init__(self, preprocess_net):
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super().__init__()
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self.preprocess = preprocess_net
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self.last = nn.Linear(128, 1)
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def forward(self, s, **kwargs):
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logits, h = self.preprocess(s, state=kwargs.get('state', None))
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logits = self.last(logits)
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return logits
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class DQN(nn.Module):
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"""For advanced usage (how to customize the network), please refer to
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:ref:`build_the_network`.
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"""
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def __init__(self, h, w, action_shape, device='cpu'):
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super(DQN, self).__init__()
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self.device = device
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self.conv1 = nn.Conv2d(4, 16, kernel_size=5, stride=2)
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self.bn1 = nn.BatchNorm2d(16)
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self.conv2 = nn.Conv2d(16, 32, kernel_size=5, stride=2)
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self.bn2 = nn.BatchNorm2d(32)
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self.conv3 = nn.Conv2d(32, 32, kernel_size=5, stride=2)
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self.bn3 = nn.BatchNorm2d(32)
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def conv2d_size_out(size, kernel_size=5, stride=2):
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return (size - (kernel_size - 1) - 1) // stride + 1
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convw = conv2d_size_out(conv2d_size_out(conv2d_size_out(w)))
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convh = conv2d_size_out(conv2d_size_out(conv2d_size_out(h)))
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linear_input_size = convw * convh * 32
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self.fc = nn.Linear(linear_input_size, 512)
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self.head = nn.Linear(512, action_shape)
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def forward(self, x, state=None, info={}):
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if not isinstance(x, torch.Tensor):
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x = torch.tensor(x, device=self.device, dtype=torch.float32)
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x = x.permute(0, 3, 1, 2)
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x = F.relu(self.bn1(self.conv1(x)))
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x = F.relu(self.bn2(self.conv2(x)))
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x = F.relu(self.bn3(self.conv3(x)))
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x = self.fc(x.reshape(x.size(0), -1))
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return self.head(x), state
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