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July 7, 2017 03:45
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import numpy as np | |
import scipy | |
import gym | |
from collections import defaultdict | |
from gym import wrappers | |
GAMMA = 0.9 | |
ALPHA = 0.1 | |
NUM_EPISODES = 500000 | |
EPSILON = 1.0 | |
EPSILON_DECAY = 0.9999 | |
def random_action(a, env, eps=0.1): | |
p = np.random.random() | |
if p < (1 - eps): | |
return a | |
else: | |
return env.action_space.sample() | |
def play_game(env, epsilon): | |
pass | |
if __name__ == '__main__': | |
env = gym.make('FrozenLake-v0') | |
env = wrappers.Monitor(env, 'frozenlake-experiment-Q1', force=True) | |
Q = np.zeros((env.observation_space.n, env.action_space.n)) | |
updateCounts = np.zeros((env.observation_space.n, env.action_space.n)) | |
deltas = [] | |
averageReturn = 0 | |
lastHalfAverage = 0 | |
for episode in xrange(NUM_EPISODES): | |
s = env.reset() | |
biggest_change = 0 | |
while True: | |
a = np.argmax(Q[s, :]) | |
a = random_action(a, env, EPSILON) | |
old_qsa = Q[s, a] | |
s2, r, done, info = env.step(a) | |
if (done and r == 0): | |
r = -1.0 | |
#alpha = (ALPHA/(updateCounts[s, a]+1)) | |
alpha = ALPHA | |
updateCounts[s, a] += 1 | |
a2 = np.argmax(Q[s2, :]) | |
maxNext = Q[s2, a2] | |
Q[s, a] = old_qsa + alpha*(r + GAMMA*maxNext - old_qsa) | |
biggest_change = max(biggest_change, np.abs(old_qsa - Q[s, a])) | |
updateCounts[s, a] = updateCounts[s, a] + 1 | |
s=s2 | |
a=a2 | |
if(done): | |
averageReturn = averageReturn + (r-averageReturn)/(episode+1) | |
EPSILON *= EPSILON_DECAY | |
if (episode+1)%10000 == 0: | |
print "Episode: ", episode+1 | |
print 'Epsilon: ', EPSILON | |
print 'Average Return: ', averageReturn | |
break | |
deltas.append(biggest_change) | |
env.close() |
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