Created
June 7, 2017 20:52
-
-
Save malzantot/8268146e82029d7fb34208e58e0720a8 to your computer and use it in GitHub Desktop.
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
import numpy as np | |
import time | |
import gym | |
def run_episode(env, policy, episode_len=100, render=False): | |
total_reward = 0 | |
obs = env.reset() | |
for t in range(episode_len): | |
if render: | |
env.render() | |
action = policy[obs] | |
obs, reward, done, _ = env.step(action) | |
total_reward += reward | |
if done: | |
break | |
return total_reward | |
def evaluate_policy(env, policy, n_episodes=100): | |
total_rewards = 0.0 | |
for _ in range(n_episodes): | |
total_rewards += run_episode(env, policy) | |
return total_rewards / n_episodes | |
def gen_random_policy(): | |
return np.random.choice(4, size=((16))) | |
if __name__ == '__main__': | |
env = gym.make('FrozenLake-v0') | |
## Policy search | |
n_policies = 2000 | |
start = time.time() | |
policy_set = [gen_random_policy() for _ in range(n_policies)] | |
policy_score = [evaluate_policy(env, p) for p in policy_set] | |
end = time.time() | |
print("Best score = %0.2f. Time taken = %4.4f seconds" %(np.max(policy_score) , end - start)) |
Sign up for free
to join this conversation on GitHub.
Already have an account?
Sign in to comment