强化学习之 Qlearning

import numpy as np
import pandas as pd

class QLearningTable:
	def __init__(self, actions, learning_rate=0.01, reward_decay=0.9, e_greedy=0.9):
		self.actions = actions
		self.lr = learning_rate
		self.gamma = reward_decay
		self.epsilon = e_greedy
		self.q_table = pd.DataFrame(columns=self.actions, dtype=np.float64)

	def choose_action(self, observation):
		self.check_state_exist(observation)
		if np.random.uniform() < self.epsilon:
			state_action = self.q_table.loc[observation,:]
			action = np.random.choice(state_action[state_action==np.max(state_action)].index)
		else:
			action = np.random.choice(self.actions)
		return action

	def learn(self, s, a, r, s_):
		self.check_state_exist(s_)
		q_predict = self.q_table.loc[s, a]
		if s_ != 'terminal':
			q_target = r + self.gamma*self.q_table.loc[s_, :].max()
		else:
			q_target = r
		self.q_table.loc[s, a] += self.lr*(q_target-q_predict)

	def check_state_exist(self, state):
		if state not in self.q_table.index:
			self.q_table = self.q_table.append(pd.Series([0]*len(self.actions),index=self.q_table.columns,name=state))