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Abstract
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| Different allocation strategies can be found in the literature to deal with the multi-armed bandit problem under a frequentist view or from a Bayesian perspective. In this paper, we propose a novel allocation strategy, the possibilistic reward method. First, possibilistic reward distributions are used to model the uncertainty about the arm expected rewards, which are then converted into probability distributions using a pignistic probability transformation. Finally, a simulation experiment is carried out to find out the one with the highest expected reward, which is then pulled. A parametric probability transformation of the proposed is then introduced together with a dynamic optimization, which implies that neither previous knowledge nor a simulation of the arm distributions is required. A numerical study proves that the proposed method outperforms other policies in the literature in five scenarios: a Bernoulli distribution with very low success probabilities, with success probabilities close to 0.5 and with success probabilities close to 0.5 and Gaussian rewards; and truncated in [0,10] Poisson and exponential distributions. | |
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International
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Si |
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Congress
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6th International Conference on Operations Research and Enterprise Systems |
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960 |
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Place
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Oporto, Portugal |
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Reviewers
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Si |
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ISBN/ISSN
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978-989-758-218-9 |
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Start Date
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23/02/2017 |
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End Date
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25/02/2017 |
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From page
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75 |
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To page
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84 |
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Proceedings of the 6th International Conference on Operations Research and Enterprise Systems |