45 lines
1.4 KiB
Python
45 lines
1.4 KiB
Python
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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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# define the actor network
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class Actor(nn.Module):
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def __init__(self, args, agent_id):
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super(Actor, self).__init__()
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self.max_action = args.high_action
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self.fc1 = nn.Linear(args.obs_shape[agent_id], 64)
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self.fc2 = nn.Linear(64, 64)
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self.fc3 = nn.Linear(64, 64)
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self.action_out = nn.Linear(128, args.action_shape[agent_id])
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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.relu(self.fc3(x))
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actions = self.max_action * torch.tanh(self.action_out(x))
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return actions
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class Critic(nn.Module):
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def __init__(self, args):
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super(Critic, self).__init__()
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self.max_action = args.high_action
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self.fc1 = nn.Linear(sum(args.obs_shape) + sum(args.action_shape), 64)
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self.fc2 = nn.Linear(64, 64)
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self.fc3 = nn.Linear(64, 64)
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self.q_out = nn.Linear(64, 1)
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def forward(self, state, action):
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state = torch.cat(state, dim=1)
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for i in range(len(action)):
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action[i] /= self.max_action
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action = torch.cat(action, dim=1)
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x = torch.cat([state, action], dim=1)
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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.relu(self.fc3(x))
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q_value = self.q_out(x)
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return q_value
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