Inferential Induction: A Novel Framework for Bayesian Reinforcement Learning
Paper i proceeding, 2020

Bayesian Reinforcement Learning (BRL) offers a decision-theoretic solution to the reinforcement learning problem. While “model-based” BRL algorithms have focused either on maintaining a posterior distribution on models, BRL “model-free” methods try to estimate value function distributions but make strong implicit assumptions or approximations. We describe a novel Bayesian framework, \emph{inferential induction}, for correctly inferring value function distributions from data, which leads to a new family of BRL algorithms. We design an algorithm, Bayesian Backwards Induction (BBI), with this framework. We experimentally demonstrate that BBI is competitive with the state of the art. However, its advantage relative to existing BRL model-free methods is not as great as we have expected, particularly when the additional computational burden is taken into account.

Författare

Emilio Jorge

Chalmers, Data- och informationsteknik, Data Science

Hannes Eriksson

Chalmers, Data- och informationsteknik, Data Science

Christos Dimitrakakis

Chalmers, Data- och informationsteknik, Data Science

Debabrota Basu

Chalmers, Data- och informationsteknik, Data Science

Divya Grover

Chalmers, Data- och informationsteknik, Data Science

Proceedings of Machine Learning Research

2640-3498 (ISSN)

Vol. 137 43-52

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C3SE (Chalmers Centre for Computational Science and Engineering)

Ämneskategorier

Bioinformatik (beräkningsbiologi)

Sannolikhetsteori och statistik

Datavetenskap (datalogi)

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2021-04-08