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Authors: Emmanuel Rachelson ; François Schnitzler ; Louis Wehenkel and Damien Ernst

Affiliation: University of Liège, Belgium

Keyword(s): Stochastic optimal control, Sample control, Reinforcement learning.

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: We introduce the Optimal Sample Selection (OSS) meta-algorithm for solving discrete-time Optimal Control problems. This meta- algorithm maps the problem of finding a near-optimal closed-loop policy to the identification of a small set of one-step system transitions, leading to high-quality policies when used as input of a batch-mode Reinforcement Learning (RL) algorithm. We detail a particular instance of this OSS meta-algorithm that uses tree-based Fitted Q-Iteration as a batch-mode RL algorithm and Cross Entropy search as a method for navigating efficiently in the space of sample sets. The results show that this particular instance of OSS algorithms is able to identify rapidly small sample sets leading to high-quality policies.

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Paper citation in several formats:
Rachelson, E.; Schnitzler, F.; Wehenkel, L. and Ernst, D. (2011). OPTIMAL SAMPLE SELECTION FOR BATCH-MODE REINFORCEMENT LEARNING. In Proceedings of the 3rd International Conference on Agents and Artificial Intelligence - Volume 2: ICAART; ISBN 978-989-8425-40-9; ISSN 2184-433X, SciTePress, pages 41-50. DOI: 10.5220/0003133500410050

@conference{icaart11,
author={Emmanuel Rachelson. and Fran\c{C}ois Schnitzler. and Louis Wehenkel. and Damien Ernst.},
title={OPTIMAL SAMPLE SELECTION FOR BATCH-MODE REINFORCEMENT LEARNING},
booktitle={Proceedings of the 3rd International Conference on Agents and Artificial Intelligence - Volume 2: ICAART},
year={2011},
pages={41-50},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0003133500410050},
isbn={978-989-8425-40-9},
issn={2184-433X},
}

TY - CONF

JO - Proceedings of the 3rd International Conference on Agents and Artificial Intelligence - Volume 2: ICAART
TI - OPTIMAL SAMPLE SELECTION FOR BATCH-MODE REINFORCEMENT LEARNING
SN - 978-989-8425-40-9
IS - 2184-433X
AU - Rachelson, E.
AU - Schnitzler, F.
AU - Wehenkel, L.
AU - Ernst, D.
PY - 2011
SP - 41
EP - 50
DO - 10.5220/0003133500410050
PB - SciTePress

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