@MikeShi42/

CartPole

Policy Search to solve OpenAI Gym's Cart Pole Problem

Files
• main.py
• _.py
• index.html
• Packager files
• requirements.txt
main.py
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```
```# Click Run Above to Start Training the Model
# After training, the best policy will automatically replay on the
# browser on the right.

import gym
import numpy as np

env = gym.make('CartPole-v1')

def play(env, policy):
observation = env.reset()

done = False
score = 0
observations = []

for _ in range(5000):
observations += [observation.tolist()] # Record the observations for normalization and replay

if done: # If the simulation was over last iteration, exit loop
break

# Pick an action according to the policy matrix
outcome = np.dot(policy, observation)
action = 1 if outcome > 0 else 0

# Make the action, record reward
observation, reward, done, info = env.step(action)
score += reward

return score, observations

print('💪💪💪 Training Policy... \n')

max = (0, [], [])

for _ in range(100):
policy = np.random.rand(1,4) - 0.5
score, observations = play(env, policy)

if score > max[0]:
max = (score, observations, policy)

print('Max Score', max[0], 'out of 500 \n')

scores = []
for _ in range(100):
score, _  = play(env, max[2])
scores += [score]

print('Average Score (100 trials)', np.mean(scores))

print('Starting Replay Server...')

import json