71 lines
1.7 KiB
Python
71 lines
1.7 KiB
Python
import numpy as np
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import matplotlib.pyplot as pl
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from typing import Callable
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float_formatter = "{:.3f}".format
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np.set_printoptions(formatter={'float_kind': float_formatter})
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K = 10
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def bandit(a: int, bias: np.array):
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zn = np.random.normal() + bias[a]
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return zn
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def test(_episode_len: int, _qu: np.array, _nu: np.array, _bandit: Callable) -> np.array:
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_epsilon = 0.5 # Anti-greediness (ability to explore)
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_rho = 0.005 # reduce epsilon with age
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_r_mean = []
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for j in range(0, _episode_len):
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# choose action
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z = np.random.uniform()
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if z <= _epsilon:
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# Choose random action
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a = np.random.randint(low=0, high=K)
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else:
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# Choose best action
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a = np.argmax(_qu)
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# get reward from bandit
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r = _bandit(a)
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_nu[a] = _nu[a] + 1
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_qu[a] = _qu[a] + (r - _qu[a]) / _nu[a]
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# Reduce tendency to explore with number of steps (or with age for humans)
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_epsilon = _epsilon * (1 - _rho)
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# Statistics
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_r_mean.append(r)
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return np.array(_r_mean)
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if __name__ == '__main__':
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# Init parameters
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num_realisations = 2000
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episode_len = 1000
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# Init r_mean
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r_mean = np.zeros(episode_len)
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for realization in range(0, num_realisations):
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# init bandits with different biases for shifting reward probability
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# -> expected reward q*(a)
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ql_star = np.random.uniform(-1.5, +1.5, K)
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# Init Q and N
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qu = np.zeros(K)
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nu = np.zeros(K)
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r_mean += test(episode_len, qu, nu, _bandit=lambda a: bandit(a, ql_star))
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pl.plot(r_mean/num_realisations)
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pl.grid()
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pl.title(f"Norm. E(R), num. realizations: Z={num_realisations}, num. bandits: K={K}")
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pl.xlabel("Episode")
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pl.ylabel("E(R)/Z")
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ax = pl.gca()
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ax.set_xlim([-episode_len/10, episode_len])
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ax.set_ylim([0, 1.5])
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pl.show()
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