Monte-Carlo utility estimates for Bayesian reinforcement learning
Paper in proceedings, 2013

This paper introduces a set of algorithms for Monte-Carlo Bayesian reinforcement learning. Firstly, Monte-Carlo estimation of upper bounds on the Bayes-optimal value function is employed to construct an optimistic policy. Secondly, gradient-based algorithms for approximate upper and lower bounds are introduced. Finally, we introduce a new class of gradient algorithms for Bayesian Bellman error minimisation. We theoretically show that the gradient methods are sound. Experimentally, we demonstrate the superiority of the upper bound method in terms of reward obtained. However, we also show that the Bayesian Bellman error method is a close second, despite its significant computational simplicity.


Christos Dimitrakakis

Chalmers, Computer Science and Engineering (Chalmers), Computing Science (Chalmers)

Proceedings of the IEEE Conference on Decision and Control

01912216 (ISSN)

7303-7308 6761048

Areas of Advance

Information and Communication Technology

Subject Categories

Computational Mathematics

Probability Theory and Statistics

Control Engineering

Computer Science





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