In [1]:
import gym
import numpy as np
import matplotlib.pyplot as plt
from gym.envs.registration import register
import random as pr
In [2]:
def rargmax(vector):
m = np.amax(vector)
indices = np.nonzero(vector == m )[0]
return pr.choice(indices)
In [3]:
register(
id = 'FrozenLake-v3',
entry_point = 'gym.envs.toy_text:FrozenLakeEnv',
kwargs = {'map_name':"4x4",'is_slippery':False}
)
env = gym.make('FrozenLake-v3')
In [5]:
Q = np.zeros([env.observation_space.n,env.action_space.n])
num_episodes = 4000
rList = []
total_move_count = []
for i in range(num_episodes):
state = env.reset()
rAll = 0
done = False
move_count = 0
while not done:
action = rargmax(Q[state,:])
new_state, reward, done,_ = env.step(action)
Q[state,action] = reward + np.max(Q[new_state,:])
rAll += reward
state = new_state
move_count+=1
rList.append(rAll)
total_move_count.append(move_count)
In [6]:
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= "red",edgecolor='none')
plt.show()
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