cartpole solver by greedily adding noise to linear function

cartpole-hill.py

import gym
import numpy as np

def run_episode(env, parameters):
    observation = env.reset()
    totalreward = 0
    counter = 0
    for _ in xrange(200):
        # env.render()
        action = 0 if np.matmul(parameters,observation) < 0 else 1
        observation, reward, done, info = env.step(action)
        totalreward += reward
        counter += 1
        if done:
            break
    return totalreward

env = gym.make('CartPole-v0')
episodes_per_update = 5
noise_scaling = 0.1
parameters = np.random.rand(4) * 2 - 1
bestreward = 0
env.monitor.start('cartpole-hill/', force=True)
for _ in xrange(10000):
    newparams = parameters + (np.random.rand(4) * 2 - 1)*noise_scaling
    # print newparams
    reward = 0
    for _ in xrange(episodes_per_update):
        run = run_episode(env,newparams)
        reward += run
    # print "reward %d best %d" % (reward, bestreward)
    if reward > bestreward:
        # print "update"
        bestreward = reward
        parameters = newparams
        if reward == 200*episodes_per_update:
            break

for _ in xrange(100):
    run_episode(env,parameters)
env.monitor.close()