COMPLEXITY OF STOCHASTIC BRANCH AND BOUND METHODS FOR BELIEF TREE SEARCH IN BAYESIAN REINFORCEMENT LEARNING

Christos Dimitrakakis

Abstract

There has been a lot of recent work on Bayesian methods for reinforcement learning exhibiting near-optimal online performance. The main obstacle facing such methods is that in most problems of interest, the optimal solution involves planning in an infinitely large tree. However, it is possible to obtain stochastic lower and upper bounds on the value of each tree node. This enables us to use stochastic branch and bound algorithms to search the tree efficiently. This paper proposes some algorithms and examines their complexity in this setting.

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Paper Citation


in Harvard Style

Dimitrakakis C. (2010). COMPLEXITY OF STOCHASTIC BRANCH AND BOUND METHODS FOR BELIEF TREE SEARCH IN BAYESIAN REINFORCEMENT LEARNING . In Proceedings of the 2nd International Conference on Agents and Artificial Intelligence - Volume 1: ICAART, ISBN 978-989-674-021-4, pages 259-264. DOI: 10.5220/0002721402590264


in Bibtex Style

@conference{icaart10,
author={Christos Dimitrakakis},
title={COMPLEXITY OF STOCHASTIC BRANCH AND BOUND METHODS FOR BELIEF TREE SEARCH IN BAYESIAN REINFORCEMENT LEARNING},
booktitle={Proceedings of the 2nd International Conference on Agents and Artificial Intelligence - Volume 1: ICAART,},
year={2010},
pages={259-264},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0002721402590264},
isbn={978-989-674-021-4},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 2nd International Conference on Agents and Artificial Intelligence - Volume 1: ICAART,
TI - COMPLEXITY OF STOCHASTIC BRANCH AND BOUND METHODS FOR BELIEF TREE SEARCH IN BAYESIAN REINFORCEMENT LEARNING
SN - 978-989-674-021-4
AU - Dimitrakakis C.
PY - 2010
SP - 259
EP - 264
DO - 10.5220/0002721402590264