62 lines
2.8 KiB
Python
62 lines
2.8 KiB
Python
"""
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Actor Network for Wireless Resource Allocation / 无线资源分配中的 Actor 网络
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This file defines the Actor network architecture for the Co-MADDPG project.
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The Actor maps local observations to deterministic resource allocation actions.
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本文档定义了 Co-MADDPG 项目中的 Actor 网络架构。
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Actor 网络将局部观测值映射到确定性的资源分配动作。
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Network Architecture / 网络架构:
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FC(obs_dim \u2192 256 \u2192 256 \u2192 128 \u2192 act_dim)
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Output Mapping / 输出映射: (Tanh + 1) / 2 \u2208 [0, 1]
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Reference / 参考文献: Section 3.2.1 Actor-Critic Structure in the project paper.
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"""
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import torch
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import torch.nn as nn
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class Actor(nn.Module):
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"""
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Actor network for mapping observations to deterministic actions in [0, 1].
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Actor 网络，将观测值映射到 [0, 1] 范围内的确定性动作。
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Architecture / 架构: FC(obs_dim \u2192 256 \u2192 256 \u2192 128 \u2192 act_dim)
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Paper Ref / 论文参考: Section 3.2.1 - Policy Network implementation.
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Args / 参数:
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obs_dim (int): Dimension of the observation space. / 观测空间的维度。
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act_dim (int): Dimension of the action space. / 动作空间的维度。
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hidden_sizes (list): Sizes of the three hidden layers (default: [256, 256, 128]). / 三个隐藏层的维度（默认：[256, 256, 128]）。
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"""
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def __init__(self, obs_dim, act_dim, hidden_sizes=[256, 256, 128]):
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super(Actor, self).__init__()
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# Ensure exactly 3 hidden layers as per model design / 确保按照模型设计包含恰好 3 个隐藏层
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assert len(hidden_sizes) == 3, "Actor requires exactly 3 hidden layer sizes"
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# Define the feedforward neural network / 定义前馈神经网络
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# FC(obs_dim \u2192 256 \u2192 256 \u2192 128 \u2192 act_dim)
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self.net = nn.Sequential(
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nn.Linear(obs_dim, hidden_sizes[0]),
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nn.ReLU(),
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nn.Linear(hidden_sizes[0], hidden_sizes[1]),
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nn.ReLU(),
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nn.Linear(hidden_sizes[1], hidden_sizes[2]),
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nn.ReLU(),
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nn.Linear(hidden_sizes[2], act_dim)
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)
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def forward(self, obs):
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"""
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Forward pass for the Actor network. / Actor 网络的前向传播。
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Args / 参数:
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obs (torch.Tensor): Local observation tensor. / 局部观测张量。
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Returns / 返回:
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torch.Tensor: Actions mapped to the [0, 1] range. / 映射到 [0, 1] 范围的动作。
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"""
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# Pass observations through the linear layers / 将观测值传入线性层
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out = self.net(obs)
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# Formula / 公式: Output (Tanh + 1) / 2 maps result to [0, 1] range / 将输出结果映射到 [0, 1] 范围
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return (torch.tanh(out) + 1.0) / 2.0
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