First Congress of Greek Mathematicians by Ioannis Emmanouil Anargyros Fellouris Apostolos Giannopoulos Sofia Lambropoulou
Author:Ioannis Emmanouil, Anargyros Fellouris, Apostolos Giannopoulos, Sofia Lambropoulou
Language: eng
Format: epub
Publisher: De Gruyter
Published: 2020-03-23T15:11:16.808000+00:00
Reinforcement learning: a comparison of UCB versus alternative adaptive policies
Wesley Cowan Department of Computer Science, Rutgers University, 110 Frelinghuysen Rd., Piscataway, NJ, 08854, USA
Michael N. Katehakis Department of Management Science and Information Systems, 100 Rockafeller Road, Piscataway, NJ, 08854, USA
Daniel Pirutinsky Department of Management Science and Information Systems, 100 Rockafeller Road, Piscataway, NJ, 08854, USA
Abstract
In this paper, we consider the basic version of Reinforcement Learning (RL) that involves computing optimal data driven (adaptive) policies for Markovian decision process with unknown transition probabilities. We provide a brief survey of the state of the art of the area, and we compare the performance of the classic UCB policy of Burnetas and Katehakis [10] with a new policy developed herein that we call MDP-Deterministic Minimum Empirical Divergence (MDP-DMED) and a method based on Posterior sampling (MDP-PS).
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