86 lines
2.3 KiB
Python
86 lines
2.3 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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REWARD_VARIANCE = 0.1
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def test(_k_arms: int, _episode_len: int, _bias: np.array, _param: tuple[float, float]) -> np.array:
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_epsilon = _param[0] # Anti-greediness (ability to explore)
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_rho = _param[1] # reduce epsilon with age
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_r_list = []
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_a_list = []
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_opt_a_list = []
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# Init Q and N
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_qu = np.zeros(_k_arms)
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_nu = np.zeros(_k_arms)
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# Calc z in advance
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z = np.random.uniform(size=_episode_len)
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# Calc a_expl in advance
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a_expl = np.random.randint(low=0, high=_k_arms, size=_episode_len)
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for j in range(0, _episode_len):
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# choose action
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_a = np.argmax(_qu) if z[j] > _epsilon else a_expl[j]
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# get reward from bandit
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_reward_list = REWARD_VARIANCE*np.random.normal(size=_k_arms) + _bias
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# get reward from action
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_reward = _reward_list[_a]
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# calc
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_nu[_a] = _nu[_a] + 1
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_qu[_a] = _qu[_a] + (_reward - _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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_a_list.append(_a)
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_r_list.append(_reward)
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opt_a = 1 if _a == np.argmax(_reward_list) else 0
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_opt_a_list.append(opt_a)
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return np.array(_r_list), np.array(_a_list), np.array(_opt_a_list)
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if __name__ == '__main__':
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# Init parameters
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k_arms = 10
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highest_reward = 1.5
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num_realisations = 2000
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episode_len = 1000
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params = [(0.0, 0.0), (0.01, 0.002), (0.1, 0.002)]
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legend = []
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for param in params:
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# Init stats
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r_mean = np.zeros(episode_len)
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o_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(-highest_reward, +highest_reward, k_arms)
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r_list, a_list, o_list = test(_k_arms=k_arms, _episode_len=episode_len, _bias=ql_star, _param=param)
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r_mean += r_list
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o_mean += o_list
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pl.plot(r_mean/num_realisations)
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legend.append(f"Param {param}")
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pl.grid()
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pl.title(f"Norm. E(R), num. realizations: Z={num_realisations}, num. bandit arms: K={k_arms}")
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pl.xlabel("Episode")
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pl.ylabel("E(R)/Z")
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pl.legend(legend)
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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, highest_reward])
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pl.show()
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