In [1]:
import gym
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
In [2]:
from gym.envs.registration import register
register(
id='FrozenLakeNotSlippery-v0',
entry_point='gym.envs.toy_text:FrozenLakeEnv',
kwargs={'map_name' : '4x4', 'is_slippery': False},
max_episode_steps=100,
reward_threshold=0.78, # optimum = .8196
)
#env = gym.make('FrozenLake-v0')
env = gym.make('FrozenLakeNotSlippery-v0')
In [3]:
Q = np.zeros([env.observation_space.n, env.action_space.n])
#Q = np.random.rand(env.observation_space.n, env.action_space.n)
lr = 0.8
y = 0.95
num_episodes = 2000
#create list to contain total rewards per episode
rList = []
for i in range(num_episodes):
#Reset environment and get first new observation
s = env.reset()
rAll = 0
d = False
j = 0
#The Q-Table learning algorithm
while j < 99:
j+=1
#Choose an action by greedily (with noise) picking from Q table
a = np.argmax(Q[s,:] + (np.random.randn(1, env.action_space.n) * (1./(i**2 + 1))))
#Get new state and reward from environment
s1, r, d, _ = env.step(a)
#Update Q-Table with new knowledge
Q[s,a] = Q[s,a] + lr*(r + y * np.max(Q[s1,:]) - Q[s,a])
rAll += r
s = s1
if d == True:
break
rList.append(rAll)
print('Score over time: {}'.format(sum(rList)/num_episodes))
print('Final Q-Table Values')
print(Q)
Score over time: 0.9545 Final Q-Table Values [[0. 0. 0.77378094 0. ] [0. 0. 0.81450625 0. ] [0. 0.857375 0. 0. ] [0. 0. 0. 0. ] [0. 0. 0. 0. ] [0. 0. 0. 0. ] [0. 0.9025 0. 0. ] [0. 0. 0. 0. ] [0. 0. 0. 0. ] [0. 0. 0. 0. ] [0. 0.95 0. 0. ] [0. 0. 0. 0. ] [0. 0. 0. 0. ] [0. 0. 0. 0. ] [0. 0. 1. 0. ] [0. 0. 0. 0. ]]
In [4]:
s = env.reset()
d = 0
j = 0
while j < 99:
#env.render()
j+=1
a = np.argmax(Q[s,:] )
#Get new state and reward from environment
s1, r, d, _ = env.step(a)
s = s1
if d == True:
break
In [5]:
plt.plot(rList)
Out[5]:
[<matplotlib.lines.Line2D at 0x7fca6de89e48>]
