update PolicyGradient

codes/PolicyGradient/agent.py

@@ -0,0 +1,68 @@
+#!/usr/bin/env python
+# coding=utf-8
+'''
+Author: John
+Email: johnjim0816@gmail.com
+Date: 2020-11-22 23:27:44
+LastEditor: John
+LastEditTime: 2020-11-23 12:05:03
+Discription: 
+Environment: 
+'''
+import torch
+from torch.distributions import Bernoulli
+from torch.autograd import Variable
+import numpy as np
+
+from model import FCN
+
+class PolicyGradient:
+    
+    def __init__(self, n_states,device='cpu',gamma = 0.99,lr = 0.01,batch_size=5):
+        self.gamma = gamma
+        self.policy_net = FCN(n_states)
+        self.optimizer = torch.optim.RMSprop(self.policy_net.parameters(), lr=lr)
+        self.batch_size = batch_size
+
+    def choose_action(self,state):
+        
+        state = torch.from_numpy(state).float()
+        state = Variable(state)
+        probs = self.policy_net(state)
+        m = Bernoulli(probs)
+        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()