update projects

projects/codes/SoftActorCritic/model.py

@@ -0,0 +1,108 @@
+#!/usr/bin/env python
+# coding=utf-8
+'''
+Author: JiangJi
+Email: johnjim0816@gmail.com
+Date: 2021-04-29 12:53:58
+LastEditor: JiangJi
+LastEditTime: 2021-11-19 18:04:19
+Discription: 
+Environment: 
+'''
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from torch.distributions import Normal
+
+device=torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+class ValueNet(nn.Module):
+    def __init__(self, n_states, hidden_dim, init_w=3e-3):
+        super(ValueNet, self).__init__()
+        
+        self.linear1 = nn.Linear(n_states, hidden_dim)
+        self.linear2 = nn.Linear(hidden_dim, hidden_dim)
+        self.linear3 = nn.Linear(hidden_dim, 1)
+
+        self.linear3.weight.data.uniform_(-init_w, init_w)
+        self.linear3.bias.data.uniform_(-init_w, init_w)
+        
+    def forward(self, state):
+        x = F.relu(self.linear1(state))
+        x = F.relu(self.linear2(x))
+        x = self.linear3(x)
+        return x
+        
+        
+class SoftQNet(nn.Module):
+    def __init__(self, n_states, n_actions, hidden_dim, init_w=3e-3):
+        super(SoftQNet, self).__init__()
+        
+        self.linear1 = nn.Linear(n_states + n_actions, hidden_dim)
+        self.linear2 = nn.Linear(hidden_dim, hidden_dim)
+        self.linear3 = nn.Linear(hidden_dim, 1)
+        
+        self.linear3.weight.data.uniform_(-init_w, init_w)
+        self.linear3.bias.data.uniform_(-init_w, init_w)
+        
+    def forward(self, state, action):
+        x = torch.cat([state, action], 1)
+        x = F.relu(self.linear1(x))
+        x = F.relu(self.linear2(x))
+        x = self.linear3(x)
+        return x
+        
+        
+class PolicyNet(nn.Module):
+    def __init__(self, n_states, n_actions, hidden_dim, init_w=3e-3, log_std_min=-20, log_std_max=2):
+        super(PolicyNet, self).__init__()
+        
+        self.log_std_min = log_std_min
+        self.log_std_max = log_std_max
+        
+        self.linear1 = nn.Linear(n_states, hidden_dim)
+        self.linear2 = nn.Linear(hidden_dim, hidden_dim)
+        
+        self.mean_linear = nn.Linear(hidden_dim, n_actions)
+        self.mean_linear.weight.data.uniform_(-init_w, init_w)
+        self.mean_linear.bias.data.uniform_(-init_w, init_w)
+        
+        self.log_std_linear = nn.Linear(hidden_dim, n_actions)
+        self.log_std_linear.weight.data.uniform_(-init_w, init_w)
+        self.log_std_linear.bias.data.uniform_(-init_w, init_w)
+        
+    def forward(self, state):
+        x = F.relu(self.linear1(state))
+        x = F.relu(self.linear2(x))
+        
+        mean    = self.mean_linear(x)
+        log_std = self.log_std_linear(x)
+        log_std = torch.clamp(log_std, self.log_std_min, self.log_std_max)
+        
+        return mean, log_std
+    
+    def evaluate(self, state, epsilon=1e-6):
+        mean, log_std = self.forward(state)
+        std = log_std.exp()
+        
+        normal = Normal(mean, std)
+        z = normal.sample()
+        action = torch.tanh(z)
+        
+        log_prob = normal.log_prob(z) - torch.log(1 - action.pow(2) + epsilon)
+        log_prob = log_prob.sum(-1, keepdim=True)
+        
+        return action, log_prob, z, mean, log_std
+        
+    
+    def get_action(self, state):
+        state = torch.FloatTensor(state).unsqueeze(0).to(device)
+        mean, log_std = self.forward(state)
+        std = log_std.exp()
+        
+        normal = Normal(mean, std)
+        z      = normal.sample()
+        action = torch.tanh(z)
+        
+        action  = action.detach().cpu().numpy()
+        return action[0]