74 lines
3.1 KiB
Python
74 lines
3.1 KiB
Python
# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# -*- coding: utf-8 -*-
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import numpy as np
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# 根据Q表格选动作
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class SarsaAgent(object):
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def __init__(self,
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obs_n,
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act_n,
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learning_rate=0.01,
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gamma=0.9,
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e_greed=0.1):
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self.act_n = act_n # 动作维度，有几个动作可选
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self.lr = learning_rate # 学习率
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self.gamma = gamma # reward的衰减率
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self.epsilon = e_greed # 按一定概率随机选动作
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self.Q = np.zeros((obs_n, act_n)) # 初始化Q表格
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# 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)
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def sample(self, obs):
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if np.random.uniform(0, 1) < (1.0 - self.epsilon): #根据table的Q值选动作
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action = self.predict(obs)
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else:
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action = np.random.choice(self.act_n) #有一定概率随机探索选取一个动作
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return action
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# 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）
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def predict(self, obs):
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Q_list = self.Q[obs, :]
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maxQ = np.max(Q_list) # 找到最大Q对应的下标
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action_list = np.where(Q_list == maxQ)[0] # maxQ可能对应多个action
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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）
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return action
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# 学习方法，也就是更新Q-table的方法
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def learn(self, obs, action, reward, next_obs, next_action, done):
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""" on-policy
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obs: 交互前的obs, s_t
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action: 本次交互选择的action, a_t
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reward: 本次动作获得的奖励r
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next_obs: 本次交互后的obs, s_t+1
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next_action: 根据当前Q表格, 针对next_obs会选择的动作, a_t+1
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done: episode是否结束
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"""
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predict_Q = self.Q[obs, action]
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if done: # done为ture的话，代表这是episode最后一个状态
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target_Q = reward # 没有下一个状态了
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else:
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target_Q = reward + self.gamma * self.Q[next_obs,
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next_action] # Sarsa
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self.Q[obs, action] += self.lr * (target_Q - predict_Q) # 修正q
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def save(self):
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npy_file = './q_table.npy'
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np.save(npy_file, self.Q)
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print(npy_file + ' saved.')
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def restore(self, npy_file='./q_table.npy'):
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self.Q = np.load(npy_file)
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print(npy_file + ' loaded.') |