update

codes/QLearning/agent1.py

@@ -0,0 +1,88 @@
+#!/usr/bin/env python
+# coding=utf-8
+'''
+Author: John
+Email: johnjim0816@gmail.com
+Date: 2020-09-11 23:03:00
+LastEditor: John
+LastEditTime: 2021-04-29 17:02:00
+Discription: 
+Environment: 
+'''
+#   Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+
+import numpy as np
+import math
+#!/usr/bin/env python
+# coding=utf-8
+'''
+Author: John
+Email: johnjim0816@gmail.com
+Date: 2020-09-11 23:03:00
+LastEditor: John
+LastEditTime: 2021-04-29 16:45:33
+Discription: use np array to define Q table
+Environment: 
+'''
+import numpy as np
+import math
+
+class QLearning(object):
+    def __init__(self,
+                 state_dim,action_dim,cfg):
+        self.action_dim = action_dim  # dimension of acgtion
+        self.lr = cfg.lr  # learning rate
+        self.gamma = cfg.gamma  
+        self.epsilon = 0 
+        self.sample_count = 0  
+        self.epsilon_start = cfg.epsilon_start
+        self.epsilon_end = cfg.epsilon_end
+        self.epsilon_decay = cfg.epsilon_decay
+        self.Q_table = np.zeros((state_dim, action_dim)) # Q表
+        
+    def choose_action(self, state):
+        self.sample_count += 1
+        self.epsilon = self.epsilon_end + (self.epsilon_start - self.epsilon_end) * \
+            math.exp(-1. * self.sample_count / self.epsilon_decay)
+        if np.random.uniform(0, 1) > self.epsilon:  # 随机选取0-1之间的值，如果大于epsilon就按照贪心策略选取action，否则随机选取
+            action = self.predict(state)
+        else:
+            action = np.random.choice(self.action_dim)  #有一定概率随机探索选取一个动作
+        return action
+
+    def predict(self, state):
+        '''根据输入观测值，采样输出的动作值，带探索，测试模型时使用
+        '''
+        Q_list = self.Q_table[state, :]
+        Q_max = np.max(Q_list)
+        action_list = np.where(Q_list == Q_max)[0]  
+        action = np.random.choice(action_list) # Q_max可能对应多个 action ，可以随机抽取一个
+        return action
+            
+    def update(self, state, action, reward, next_state, done):
+        Q_predict = self.Q_table[state, action]
+        if done:
+            Q_target = reward  # 没有下一个状态了
+        else:
+            Q_target = reward + self.gamma * np.max(
+                self.Q_table[next_state, :])  # Q_table-learning
+        self.Q_table[state, action] += self.lr * (Q_target - Q_predict)  # 修正q
+    def save(self,path):
+        np.save(path+"Q_table.npy", self.Q_table)
+    def load(self, path):
+        self.Q_table = np.load(path+"Q_table.npy")
+
+