update Sarsa

codes/Sarsa/agent.py

@@ -1,74 +1,52 @@
-#   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.
-
-# -*- coding: utf-8 -*-
-
+#!/usr/bin/env python
+# coding=utf-8
+'''
+Author: John
+Email: johnjim0816@gmail.com
+Date: 2021-03-12 16:58:16
+LastEditor: John
+LastEditTime: 2021-03-12 17:03:05
+Discription: 
+Environment: 
+'''
 import numpy as np
-
-# 根据Q表格选动作
-class SarsaAgent(object):
+from collections import defaultdict
+import torch
+class Sarsa(object):
     def __init__(self,
-                 obs_n,
-                 act_n,
-                 learning_rate=0.01,
-                 gamma=0.9,
-                 e_greed=0.1):
-        self.act_n = act_n  # 动作维度，有几个动作可选
-        self.lr = learning_rate  # 学习率
-        self.gamma = gamma  # reward的衰减率
-        self.epsilon = e_greed  # 按一定概率随机选动作
-        self.Q = np.zeros((obs_n, act_n))  # 初始化Q表格
-
-    # 根据输入观察值，采样输出的动作值，带探索(epsilon-greedy，训练时用这个方法)
-    def sample(self, obs):
-        if np.random.uniform(0, 1) < (1.0 - self.epsilon):  #根据table的Q值选动作
-            action = self.predict(obs)
-        else:
-            action = np.random.choice(self.act_n)  #有一定概率随机探索选取一个动作
+                 n_actions,sarsa_cfg,):
+        self.n_actions = n_actions  # number of actions
+        self.lr = sarsa_cfg.lr  # learning rate
+        self.gamma = sarsa_cfg.gamma  
+        self.epsilon = sarsa_cfg.epsilon  
+        self.Q  = defaultdict(lambda: np.zeros(n_actions))
+        # self.Q = np.zeros((n_states, n_actions))  # Q表
+    def choose_action(self, state):
+        best_action = np.argmax(self.Q[state])
+        # action = best_action
+        action_probs = np.ones(self.n_actions, dtype=float) * self.epsilon / self.n_actions
+        action_probs[best_action] += (1.0 - self.epsilon)
+        action = np.random.choice(np.arange(len(action_probs)), p=action_probs) 
         return action
-
-    # 根据输入观察值，预测输出的动作值（已有里面挑最大，贪心的算法，只有利用，没有探索）
-    def predict(self, obs):
-        Q_list = self.Q[obs, :]
-        maxQ = np.max(Q_list)  # 找到最大Q对应的下标 
-        action_list = np.where(Q_list == maxQ)[0]  # maxQ可能对应多个action
-        action = np.random.choice(action_list)  # 从这些action中随机挑一个action（可以打印出来看看）
-        return action
-
-    # 学习方法，也就是更新Q-table的方法
-    def learn(self, obs, action, reward, next_obs, next_action, done):
-        """ on-policy
-            obs: 交互前的obs, s_t
-            action: 本次交互选择的action, a_t
-            reward: 本次动作获得的奖励r
-            next_obs: 本次交互后的obs, s_t+1
-            next_action: 根据当前Q表格, 针对next_obs会选择的动作, a_t+1
-            done: episode是否结束
-        """
-        predict_Q = self.Q[obs, action]
-        if done:  # done为ture的话，代表这是episode最后一个状态
-            target_Q = reward  # 没有下一个状态了
+            
+    def update(self, state, action, reward, next_state, next_action,done):
+        Q_predict = self.Q[state][action]
+        if done:
+            Q_target = reward  # terminal state
         else:
-            target_Q = reward + self.gamma * self.Q[next_obs,
-                                                    next_action]  # Sarsa
-        self.Q[obs, action] += self.lr * (target_Q - predict_Q)  # 修正q
-
-    def save(self):
-        npy_file = './q_table.npy'
-        np.save(npy_file, self.Q)
-        print(npy_file + ' saved.')
-
-    def restore(self, npy_file='./q_table.npy'):
-        self.Q = np.load(npy_file)
-        print(npy_file + ' loaded.')
+            Q_target = reward + self.gamma * self.Q[next_state][next_action] 
+        self.Q[state][action] += self.lr * (Q_target - Q_predict) 
+    def save(self,path):
+        '''把 Q表格 的数据保存到文件中
+        '''
+        import dill
+        torch.save(
+            obj=self.Q,
+            f=path+"Sarsa_model.pkl",
+            pickle_module=dill
+        )
+    def load(self, path):
+        '''从文件中读取数据到 Q表格
+        '''
+        import dill
+        self.Q =torch.load(f=path+'Sarsa_model.pkl',pickle_module=dill)