60 lines
2.2 KiB
Python
60 lines
2.2 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: 2021-09-15 13:18:37
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Discription: use defaultdict 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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import torch
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from collections import defaultdict
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class QLearning(object):
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def __init__(self,state_dim,
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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 = defaultdict(lambda: np.zeros(action_dim)) # A nested dictionary that maps state -> (action -> action-value)
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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) # epsilon是会递减的，这里选择指数递减
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# e-greedy 策略
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if np.random.uniform(0, 1) > self.epsilon:
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action = np.argmax(self.Q_table[str(state)]) # 选择Q(s,a)最大对应的动作
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else:
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action = np.random.choice(self.action_dim) # 随机选择动作
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return action
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def predict(self,state):
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action = np.argmax(self.Q_table[str(state)])
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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[str(state)][action]
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if done: # 终止状态
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Q_target = reward
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else:
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Q_target = reward + self.gamma * np.max(self.Q_table[str(next_state)])
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self.Q_table[str(state)][action] += self.lr * (Q_target - Q_predict)
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def save(self,path):
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import dill
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torch.save(
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obj=self.Q_table,
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f=path+"Qleaning_model.pkl",
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pickle_module=dill
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)
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print("保存模型成功！")
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def load(self, path):
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import dill
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self.Q_table =torch.load(f=path+'Qleaning_model.pkl',pickle_module=dill)
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print("加载模型成功！") |