Created
September 15, 2018 18:35
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import gym | |
import numpy | |
impoort time | |
#Function for a random policy | |
def randomPolicy(): | |
return numpy.random.choice(4, size=((16))) | |
#Execution | |
def execute(env, policy, episode_len=100, render=False): | |
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 | |
#Evaluation | |
def optimalPolicy(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 | |
if __name__ == '__main__': | |
env = gym.make('FrozenLake-v0') | |
## Policy search | |
maxIteration = 1000 | |
start = time.time() | |
policy_set = [randomPolicy() for _ in range(maxIteration)] | |
policy_score = [optimalPolicy(env, p) for p in policy_set] | |
end = time.time() | |
print("Best score = %0.2f. Time taken = %4.4f seconds" %(numpy.max(policy_score) , end - start)) |
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