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50 行
1.9 KiB
50 行
1.9 KiB
import torch
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from torch import nn
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from torch import distributions
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EPSILON = 1e-6 # Small value to avoid divide by zero
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class GaussianDistribution(nn.Module):
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def __init__(self, hidden_size, num_outputs, **kwargs):
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super(GaussianDistribution, self).__init__(**kwargs)
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self.mu = nn.Linear(hidden_size, num_outputs)
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self.log_sigma_sq = nn.Linear(hidden_size, num_outputs)
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nn.init.xavier_uniform(self.mu.weight, gain=0.01)
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nn.init.xavier_uniform(self.log_sigma_sq.weight, gain=0.01)
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def forward(self, inputs):
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mu = self.mu(inputs)
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log_sig = self.log_sigma_sq(inputs)
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return [
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distributions.normal.Normal(loc=mu, scale=torch.sqrt(torch.exp(log_sig)))
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]
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class MultiCategoricalDistribution(nn.Module):
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def __init__(self, hidden_size, act_sizes):
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super(MultiCategoricalDistribution, self).__init__()
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self.branches = self.create_policy_branches(hidden_size, act_sizes)
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def create_policy_branches(self, hidden_size, act_sizes):
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branches = []
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for size in act_sizes:
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branch_output_layer = nn.Linear(hidden_size, size)
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nn.init.xavier_uniform(branch_output_layer.weight, gain=0.01)
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branches.append(branch_output_layer)
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return branches
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def mask_branch(self, logits, mask):
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raw_probs = torch.sigmoid(logits, dim=-1) * mask
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normalized_probs = raw_probs / torch.sum(raw_probs, dim=-1)
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normalized_logits = torch.log(normalized_probs)
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return normalized_logits
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def forward(self, inputs, masks):
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branch_distributions = []
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for idx, branch in enumerate(self.branches):
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logits = branch(inputs)
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norm_logits = self.mask_branch(logits, masks[idx])
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distribution = distributions.categorical.Categorical(logits=norm_logits)
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branch_distributions.append(distribution)
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return branch_distributions
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