48 lines
1.5 KiB
Python
48 lines
1.5 KiB
Python
#!/usr/bin/env python
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# coding=utf-8
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'''
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Author: John
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Email: johnjim0816@gmail.com
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Date: 2021-03-12 21:14:12
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LastEditor: John
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LastEditTime: 2021-03-13 13:48:35
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Discription:
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Environment:
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'''
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import torch.nn as nn
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import torch.nn.functional as F
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class MLP1(nn.Module):
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''' 多层感知机
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输入：state维度
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输出：概率
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'''
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def __init__(self,n_states,hidden_dim = 36):
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super(MLP1, self).__init__()
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# 24和36为hidden layer的层数，可根据state_dim, n_actions的情况来改变
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self.fc1 = nn.Linear(n_states, hidden_dim)
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self.fc2 = nn.Linear(hidden_dim,hidden_dim)
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self.fc3 = nn.Linear(hidden_dim, 1) # Prob of Left
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def forward(self, x):
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x = F.relu(self.fc1(x))
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x = F.relu(self.fc2(x))
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x = F.sigmoid(self.fc3(x))
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return x
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class MLP2(nn.Module):
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def __init__(self, n_states,n_actions,hidden_dim=128):
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""" 初始化q网络，为全连接网络
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n_states: 输入的feature即环境的state数目
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n_actions: 输出的action总个数
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"""
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super(MLP2, self).__init__()
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self.fc1 = nn.Linear(n_states, hidden_dim) # 输入层
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self.fc2 = nn.Linear(hidden_dim,hidden_dim) # 隐藏层
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self.fc3 = nn.Linear(hidden_dim, n_actions) # 输出层
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def forward(self, x):
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# 各层对应的激活函数
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x = F.relu(self.fc1(x))
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x = F.relu(self.fc2(x))
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return self.fc3(x) |