update projects

projects/codes/PolicyGradient/pg.py

@@ -0,0 +1,89 @@
+#!/usr/bin/env python
+# coding=utf-8
+'''
+Author: John
+Email: johnjim0816@gmail.com
+Date: 2020-11-22 23:27:44
+LastEditor: John
+LastEditTime: 2022-02-10 01:25:27
+Discription: 
+Environment: 
+'''
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from torch.distributions import Bernoulli
+from torch.autograd import Variable
+import numpy as np
+
+class MLP(nn.Module):
+    
+    ''' 多层感知机
+        输入：state维度
+        输出：概率
+    '''
+    def __init__(self,input_dim,hidden_dim = 36):
+        super(MLP, self).__init__()
+        # 24和36为hidden layer的层数，可根据input_dim, n_actions的情况来改变
+        self.fc1 = nn.Linear(input_dim, hidden_dim)
+        self.fc2 = nn.Linear(hidden_dim,hidden_dim)
+        self.fc3 = nn.Linear(hidden_dim, 1)  # Prob of Left
+
+    def forward(self, x):
+        x = F.relu(self.fc1(x))
+        x = F.relu(self.fc2(x))
+        x = F.sigmoid(self.fc3(x))
+        return x
+        
+class PolicyGradient:
+    
+    def __init__(self, n_states,cfg):
+        self.gamma = cfg.gamma
+        self.policy_net = MLP(n_states,hidden_dim=cfg.hidden_dim)
+        self.optimizer = torch.optim.RMSprop(self.policy_net.parameters(), lr=cfg.lr)
+        self.batch_size = cfg.batch_size
+
+    def choose_action(self,state):
+        
+        state = torch.from_numpy(state).float()
+        state = Variable(state)
+        probs = self.policy_net(state)
+        m = Bernoulli(probs) # 伯努利分布
+        action = m.sample()
+        action = action.data.numpy().astype(int)[0] # 转为标量
+        return action
+        
+    def update(self,reward_pool,state_pool,action_pool):
+        # Discount reward
+        running_add = 0
+        for i in reversed(range(len(reward_pool))):
+            if reward_pool[i] == 0:
+                running_add = 0
+            else:
+                running_add = running_add * self.gamma + reward_pool[i]
+                reward_pool[i] = running_add
+
+        # Normalize reward
+        reward_mean = np.mean(reward_pool)
+        reward_std = np.std(reward_pool)
+        for i in range(len(reward_pool)):
+            reward_pool[i] = (reward_pool[i] - reward_mean) / reward_std
+
+        # Gradient Desent
+        self.optimizer.zero_grad()
+
+        for i in range(len(reward_pool)):
+            state = state_pool[i]
+            action = Variable(torch.FloatTensor([action_pool[i]]))
+            reward = reward_pool[i]
+            state = Variable(torch.from_numpy(state).float())
+            probs = self.policy_net(state)
+            m = Bernoulli(probs)
+            loss = -m.log_prob(action) * reward  # Negtive score function x reward
+            # print(loss)
+            loss.backward()
+        self.optimizer.step()
+    def save(self,path):
+        torch.save(self.policy_net.state_dict(), path+'pg_checkpoint.pt')
+    def load(self,path):
+        self.policy_net.load_state_dict(torch.load(path+'pg_checkpoint.pt'))