58 lines
2.1 KiB
Python
58 lines
2.1 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: 2021-03-12 16:58:16
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LastEditor: John
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LastEditTime: 2022-08-04 22:22:16
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Discription:
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Environment:
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'''
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import numpy as np
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from collections import defaultdict
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import torch
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import math
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class Sarsa(object):
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def __init__(self,
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n_actions,cfg):
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self.n_actions = n_actions
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self.lr = cfg.lr
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self.gamma = cfg.gamma
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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 = defaultdict(lambda: np.zeros(n_actions)) # Q table
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def sample(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) # The probability to select a random action, is is log decayed
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best_action = np.argmax(self.Q[state])
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action_probs = np.ones(self.n_actions, dtype=float) * self.epsilon / self.n_actions
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action_probs[best_action] += (1.0 - self.epsilon)
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action = np.random.choice(np.arange(len(action_probs)), p=action_probs)
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return action
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def predict(self,state):
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return np.argmax(self.Q[state])
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def update(self, state, action, reward, next_state, next_action,done):
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Q_predict = self.Q[state][action]
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if done:
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Q_target = reward # 终止状态
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else:
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Q_target = reward + self.gamma * self.Q[next_state][next_action] # 与Q learning不同，Sarsa是拿下一步动作对应的Q值去更新
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self.Q[state][action] += self.lr * (Q_target - Q_predict)
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def save(self,path):
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'''把 Q表格 的数据保存到文件中
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'''
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import dill
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torch.save(
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obj=self.Q,
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f=path+"sarsa_model.pkl",
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pickle_module=dill
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)
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def load(self, path):
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'''从文件中读取数据到 Q表格
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'''
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import dill
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self.Q =torch.load(f=path+'sarsa_model.pkl',pickle_module=dill) |