mirror of
https://github.com/wassname/pytorch-a2c-ppo-acktr.git
synced 2026-06-27 16:20:05 +08:00
Ilya Kostrikov
53 lines
1.5 KiB
Python
Executable File
53 lines
1.5 KiB
Python
Executable File
import math
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import torch
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import torch.nn as nn
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import torch.nn.functional as F
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def weights_init(m):
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classname = m.__class__.__name__
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if classname.find('Conv') != -1 or classname.find('Linear') != -1:
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nn.init.orthogonal(m.weight.data)
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if m.bias is not None:
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m.bias.data.fill_(0)
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class ActorCritic(torch.nn.Module):
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def __init__(self, num_inputs, action_space):
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super(ActorCritic, self).__init__()
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self.conv1 = nn.Conv2d(num_inputs, 32, 8, stride=4)
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self.conv2 = nn.Conv2d(32, 64, 4, stride=2)
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self.conv3 = nn.Conv2d(64, 64, 3, stride=1)
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self.linear1 = nn.Linear(64 * 7 * 7, 512)
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num_outputs = action_space.n
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self.critic_linear = nn.Linear(512, 1)
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self.actor_linear = nn.Linear(512, num_outputs)
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self.apply(weights_init)
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self.conv1.weight.data.mul_(math.sqrt(2)) # Multiplier for relu
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self.conv2.weight.data.mul_(math.sqrt(2)) # Multiplier for relu
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self.conv3.weight.data.mul_(math.sqrt(2)) # Multiplier for relu
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self.linear1.weight.data.mul_(math.sqrt(2)) # Multiplier for relu
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self.train()
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def forward(self, inputs):
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x = self.conv1(inputs / 255.0)
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x = F.relu(x)
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x = self.conv2(x)
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x = F.relu(x)
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x = self.conv3(x)
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x = F.relu(x)
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x = x.view(-1, 64 * 7 * 7)
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x = self.linear1(x)
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x = F.relu(x)
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return self.critic_linear(x), self.actor_linear(x)
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