[Lecture 4 Q learning] #강화학습

Lecture 4 Q learning.py

# -*- coding: utf-8 -*-


"""Lecture 4 Q learning"""

import gym
import numpy as np
import random
import matplotlib.pyplot as plt
from gym.envs.registration import register



register(
	id = "FrozenLake-v3",
	entry_point = "gym.envs.toy_text:FrozenLakeEnv",
	kwargs = {"map_name": "4x4", "is_slippery": False}
)



env = gym.make("FrozenLake-v3")



# Q 테이블 0 으로 초기화
Q = np.zeros([env.observation_space.n, env.action_space.n])



# 디스카운터 팩터
dis = .99
num_episodes = 2000



# 에피소드 별 보상과 스텝 정보를 저장
rList = []



for i in range(num_episodes):
	# 환경 초기화
	state = env.reset()
	rAll = 0
	done = False

	# Q 테이블 학습
	while not done:
		# 노이즈 포함 방식
		action = np.argmax((Q[state, :]) + np.random.randn(1, env.action_space.n) / (1 + i))

		# reward 받기
		new_state, reward, done, _ = env.step(action)

		# Q 테이블 업데이트
		Q[state, action] = reward + dis * np.max(Q[new_state, :])

		rAll += reward
		state = new_state
	rList.append(rAll)

print("Success rate: " + str(sum(rList) / num_episodes))
print("Final Q-Table Values")
print(Q)
plt.bar(range(len(rList)), rList, color="blue")
plt.show()