Exploration Through Reward Biasing: Reward-Biased Maximum Likelihood Estimation in Stochastic Multi-Armed Bandits

Jul 12, 2020



We propose a new family of bandit algorithms, that are formulated in a general way based on the Biased Maximum Likelihood Estimation (BMLE) method originally appearing in the adaptive control literature. We design the reward-bias term to tackle the exploration and exploitation tradeoff for stochastic bandit problems. We provide a general recipe for the BMLE algorithm and derive a simple explicit closed-form expression for the index of an arm for exponential family reward distributions. We prove that the derived BMLE indices achieve a logarithmic finite-time regret bound and hence attain order-optimality, for both exponential families and the cases beyond parametric distributions. Through extensive simulations, we demonstrate that the proposed algorithms achieve regret performance comparable to the best of several state-of-the-art baseline methods, while being computationally efficient in comparison to other best-performing methods. The generality of the proposed approach makes it possible to address more complex models, including general adaptive control of Markovian systems.



About ICML 2020

The International Conference on Machine Learning (ICML) is the premier gathering of professionals dedicated to the advancement of the branch of artificial intelligence known as machine learning. ICML is globally renowned for presenting and publishing cutting-edge research on all aspects of machine learning used in closely related areas like artificial intelligence, statistics and data science, as well as important application areas such as machine vision, computational biology, speech recognition, and robotics. ICML is one of the fastest growing artificial intelligence conferences in the world. Participants at ICML span a wide range of backgrounds, from academic and industrial researchers, to entrepreneurs and engineers, to graduate students and postdocs.

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