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Authors: Yann-Michaël De Hauwere ; Peter Vrancx and Ann Nowé

Affiliation: Vrije Universiteit Brussel, Belgium

Keyword(s): Multi-agent Reinforcement Learning.

Related Ontology Subjects/Areas/Topics: Agents ; Artificial Intelligence ; Artificial Intelligence and Decision Support Systems ; Autonomous Systems ; Computational Intelligence ; Distributed and Mobile Software Systems ; Enterprise Information Systems ; Evolutionary Computing ; Knowledge Discovery and Information Retrieval ; Knowledge Engineering and Ontology Development ; Knowledge-Based Systems ; Machine Learning ; Multi-Agent Systems ; Soft Computing ; Software Engineering ; Symbolic Systems

Abstract: When multiple agents act in the same environment, single-agent reinforcement learning (RL) techniques often fail, as they do not take into account other agents. An agent using single agent RL generally does not have sufficient information to obtain a good policy. However, multi-agent techniques that simply extend the state space to include information on the other agents suffer from a large overhead, leading to very slow learning. In this paper we describe a multi-level RL algorithm which acts independently whenever possible and learns in which states it should enrich its state information with information about other agents. Such states, which we call conflict states are detected using statistical information about expected payoffs in these states. We demonstrate through experiments that our approach learns a good trade-off between learning in the single-agent state space and learning in the multi-agent state space.

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Paper citation in several formats:
De Hauwere, Y.; Vrancx, P. and Nowé, A. (2011). ADAPTIVE STATE REPRESENTATIONS FOR MULTI-AGENT REINFORCEMENT LEARNING. In Proceedings of the 3rd International Conference on Agents and Artificial Intelligence - Volume 1: ICAART; ISBN 978-989-8425-41-6; ISSN 2184-433X, SciTePress, pages 181-189. DOI: 10.5220/0003145701810189

@conference{icaart11,
author={Yann{-}Michaël {De Hauwere}. and Peter Vrancx. and Ann Nowé.},
title={ADAPTIVE STATE REPRESENTATIONS FOR MULTI-AGENT REINFORCEMENT LEARNING},
booktitle={Proceedings of the 3rd International Conference on Agents and Artificial Intelligence - Volume 1: ICAART},
year={2011},
pages={181-189},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0003145701810189},
isbn={978-989-8425-41-6},
issn={2184-433X},
}

TY - CONF

JO - Proceedings of the 3rd International Conference on Agents and Artificial Intelligence - Volume 1: ICAART
TI - ADAPTIVE STATE REPRESENTATIONS FOR MULTI-AGENT REINFORCEMENT LEARNING
SN - 978-989-8425-41-6
IS - 2184-433X
AU - De Hauwere, Y.
AU - Vrancx, P.
AU - Nowé, A.
PY - 2011
SP - 181
EP - 189
DO - 10.5220/0003145701810189
PB - SciTePress

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