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

Christos Dimitrakakis

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Paper

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

Topics: Machine Learning; Planning and Scheduling; Uncertainty in AI

In Proceedings of the 2nd International Conference on Agents and Artificial Intelligence - Volume 1: ICAART, 259-264, 2010 , Valencia, Spain

Author: Christos Dimitrakakis

Affiliation: University of Amsterdam, Netherlands

Keyword(s): Exploration, Bayesian reinforcement learning, Belief tree search, Complexity, PAC bounds.

Related Ontology Subjects/Areas/Topics: Artificial Intelligence ; Computational Intelligence ; Evolutionary Computing ; Formal Methods ; Informatics in Control, Automation and Robotics ; Intelligent Control Systems and Optimization ; Knowledge Discovery and Information Retrieval ; Knowledge-Based Systems ; Machine Learning ; Planning and Scheduling ; Simulation and Modeling ; Soft Computing ; Symbolic Systems ; Uncertainty in AI

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 several formats:

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; ISSN 2184-433X, SciTePress, pages 259-264. DOI: 10.5220/0002721402590264

@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},
issn={2184-433X},
}

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
IS - 2184-433X
AU - Dimitrakakis, C.
PY - 2010
SP - 259
EP - 264
DO - 10.5220/0002721402590264
PB - SciTePress