89 lines
3.0 KiB
Python
89 lines
3.0 KiB
Python
#!/usr/bin/env python
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# coding=utf-8
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'''
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Author: John
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Email: johnjim0816@gmail.com
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Date: 2020-09-11 23:03:00
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LastEditor: John
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LastEditTime: 2021-04-29 17:02:00
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Discription:
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Environment:
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'''
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# 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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import numpy as np
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import math
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#!/usr/bin/env python
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# coding=utf-8
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'''
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Author: John
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Email: johnjim0816@gmail.com
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Date: 2020-09-11 23:03:00
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LastEditor: John
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LastEditTime: 2021-04-29 16:45:33
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Discription: use np array to define Q table
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Environment:
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'''
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import numpy as np
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import math
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class QLearning(object):
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def __init__(self,
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state_dim,action_dim,cfg):
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self.action_dim = action_dim # dimension of acgtion
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self.lr = cfg.lr # learning rate
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self.gamma = cfg.gamma
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self.epsilon = 0
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self.sample_count = 0
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self.epsilon_start = cfg.epsilon_start
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self.epsilon_end = cfg.epsilon_end
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self.epsilon_decay = cfg.epsilon_decay
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self.Q_table = np.zeros((state_dim, action_dim)) # Q表
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def choose_action(self, state):
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self.sample_count += 1
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self.epsilon = self.epsilon_end + (self.epsilon_start - self.epsilon_end) * \
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math.exp(-1. * self.sample_count / self.epsilon_decay)
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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
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action = self.predict(state)
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else:
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action = np.random.choice(self.action_dim) #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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def predict(self, state):
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'''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
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'''
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Q_list = self.Q_table[state, :]
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Q_max = np.max(Q_list)
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action_list = np.where(Q_list == Q_max)[0]
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action = np.random.choice(action_list) # Q_max可能对应多个 action ，可以随机抽取一个
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return action
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def update(self, state, action, reward, next_state, done):
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Q_predict = self.Q_table[state, action]
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if done:
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Q_target = reward # 没有下一个状态了
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else:
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Q_target = reward + self.gamma * np.max(
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self.Q_table[next_state, :]) # Q_table-learning
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self.Q_table[state, action] += self.lr * (Q_target - Q_predict) # 修正q
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def save(self,path):
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np.save(path+"Q_table.npy", self.Q_table)
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def load(self, path):
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self.Q_table = np.load(path+"Q_table.npy")
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