57 lines
1.2 KiB
Python
57 lines
1.2 KiB
Python
import numpy as np
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import matplotlib.pyplot as pl
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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(bias):
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zn = np.random.uniform() + bias
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return zn
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if __name__ == '__main__':
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epsilon = 0.0 # Anti-greediness
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rho = 0.01 # reduce epsilon with age
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qu = np.zeros(K)
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nu = np.zeros(K)
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r_vec = []
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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 = [-0.4, -0.3, -0.2, -0.1, 0.0, +0.1, +0.2, +0.3, +0.4, +0.5]
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N = 1000
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n_ages = 1000
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for age in range(0, n_ages):
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r_sum = 0
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for j in range(0, N):
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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(ql_star[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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r_sum += r
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r_vec.append(r_sum/N)
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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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print(f"ql_star = {ql_star}")
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print(f"qu = {qu}")
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print(f"nu = {nu}")
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pl.plot(np.array(r_vec))
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pl.grid()
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pl.show()
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