add some code

docs/code/Sarsa/agent.py

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+#   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 -*-
+
+import numpy as np
+
+# 根据Q表格选动作
+class SarsaAgent(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
+        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
+        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.')