In [1]:
import logging
import gym
import numpy as np
import matplotlib.pyplot as plt
# do not repeat this at home
import gym.envs.registration
gym.envs.registration.logger.setLevel(logging.ERROR)
def run_episode(env, parameters, limit=200):
observation = env.reset()
total_reward = 0
for _ in range(limit):
action = 0 if np.matmul(parameters, observation) < 0 else 1
observation, reward, done, info = env.step(action)
total_reward += reward
if done:
break
return total_reward
def train(limit=200):
env = gym.make('CartPole-v0')
reward = 0
params = 0
counter = 0
while True:
counter += 1
current_params = np.random.rand(4) * 2 - 1
current_reward = run_episode(env, current_params, limit)
if current_reward > reward:
reward = current_reward
params = current_params
if reward == limit:
break
return counter, params
def play(limit=200):
_, params = train(limit=limit)
env = gym.make('CartPole-v0')
env = gym.wrappers.Monitor(env, '/tmp', force=True)
run_episode(env, params, limit=limit)
def evaluate():
results = [train()[0] for _ in range(500)]
plt.hist(results, 50, normed=1, facecolor='b', alpha=0.75)
plt.xlabel('Episodes required to reach 200')
plt.ylabel('Frequency')
plt.title('Random Search')
plt.show()
# play()
evaluate()
[-0.20780509 -0.54978318 0.69171046 -0.06814823]
[-0.09122666 -0.68933195 -0.74631435 0.53155022]
[-0.90210913 0.51599814 -0.8790003 0.93151267]
[-0.33706754 0.50127941 0.52852355 -0.23118985]
[-0.87972828 0.02531456 0.37876656 0.25779081]
[-0.44233087 0.4847468 0.52915485 -0.59307042]
[ 0.37043764 0.4746183 -0.21750316 -0.34638011]
[ 0.82940888 -0.58539403 0.01258169 -0.04844753]
[-0.52383184 0.38275566 0.86568542 0.68100057]
[ 0.96770735 -0.48299791 0.75588508 0.53442202]
[ 0.48800017 -0.98488314 -0.46204961 -0.69610669]
[-0.33166633 -0.46940872 -0.95942825 0.1111525 ]
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[-0.75499867 0.48591057 0.67476683 0.58653612]
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[ 0.31010331 -0.05194593 0.19001796 0.29963199]
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Content source: park-python/course
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