[Lecture 3 Dummy Q-learning] #강화학습

Lecture 3 Dummy Q-learning.py

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

"""Lecture 3 Dummy Q-learning (table)"""


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



# argmax that chooses randomly among eligible(적격의) maxium indices
def rargmax(vector):
	m = np.amax(vector)
	indices = np.nonzero(vector == m)[0]
	return pr.choice(indices)



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



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



#Initialize table with all zeros
Q = np.zeros([env.observation_space.n, env.action_space.n])

#Set learning parameters
num_episodes = 2000

# Q

# num_episodes

# create lists to contain total rewards and steps per episode
rList = []

for i in range(num_episodes):
	#Reset environment and get first new observation
	state = env.reset()
	rAll = 0
	done = False

	#The Q-Table learning algorithm
	while not done:
		action = rargmax(Q[state, :])

		#Get new state and reward from environment
		new_state, reward, done, _ = env.step(action)

		#Update Q-Table with new knowledge using learning rate
		Q[state, action] = reward + 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("LEFT DOWN RIGHT UP")
print(Q)
plt.bar(range(len(rList)), rList, color="blue")
plt.show()