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48 lines
1.4 KiB
48 lines
1.4 KiB
3 years ago
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#!/usr/bin/env python
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import pickle
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import gym
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import numpy as np
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import pandas as pd
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from scipy.stats import gaussian_kde as gkde
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def train(data):
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D = pd.DataFrame(data)
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D['qoi'] = D['obs'].apply(lambda o: np.sum(o,axis=0)/np.sqrt(len(o)))
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D['i'] = D['lam'].apply(lambda l: norm.pdf(l).prod())
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D['o'] = D['qoi'].apply(lambda q: norm.pdf(q).prod())
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Q = np.array(D['qoi'].to_list()).reshape(-1,4)
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K = [gkde(Q[:,i]) for i in range(4)]
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D['p'] = D['qoi'].apply(lambda q: np.prod([ K[i].pdf(q[i]) for i in range(4)]))
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D['u'] = D['i']*D['o']/D['p']
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mud_point_idx = D['u'].argmax()
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mud_point = D['lam'].iloc[mud_point_idx]
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return mud_point
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def test(decision=np.array([-0.09, -0.71, -0.43 , -0.74]), seed=1992):
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env = gym.make("CartPole-v1")
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observation, info = env.reset(seed=seed, return_info=True)
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score = 1
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for i in range(10000):
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action = 1 if decision.T @ observation < 0 else 0
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observation, reward, done, info = env.step(action)
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score += reward
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env.render()
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if done:
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if score == 500:
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print("WIN")
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else:
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print("LOSE: {int(score)}")
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score = 1 # reset score
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observation, info = env.reset(return_info=True)
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env.close()
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if __name__ == "__main__":
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data = pickle.load(open('data.pkl','rb'))
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mud_point = train(data)
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print("MUD Point: {mud_point}")
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test(mud_point)
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