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Authors: Douglas M. Guisi 1 ; Richardson Ribeiro 1 ; Marcelo Teixeira 1 ; André P. Borges 1 ; Eden R. Dosciatti 1 and Fabrício Enembreck 2

Affiliations: 1 Federal University of Technology-Paraná, Brazil ; 2 Pontifical Catholic University-Paraná, Brazil

ISBN: 978-989-758-187-8

Keyword(s): Multi-Agents Systems, Coordination Model, Reinforcement Learning, Hybrid Model.

Related Ontology Subjects/Areas/Topics: Agents ; Artificial Intelligence ; Artificial Intelligence and Decision Support Systems ; Distributed and Mobile Software Systems ; Enterprise Information Systems ; Intelligent Agents ; Internet Technology ; Knowledge Engineering and Ontology Development ; Knowledge-Based Systems ; Multi-Agent Systems ; Software Engineering ; Symbolic Systems ; Web Information Systems and Technologies

Abstract: The main contribution of this paper is to implement a hybrid method of coordination from the combination of interaction models developed previously. The interaction models are based on the sharing of rewards for learning with multiple agents in order to discover interactively good quality policies. Exchange of rewards among agents, when not occur properly, can cause delays in learning or even cause unexpected behavior, making the cooperation inefficient and converging to a non-satisfactory policy. From these concepts, the hybrid method uses the characteristics of each model, reducing possible conflicts between different policy actions with rewards, improving the coordination of agents in reinforcement learning problems. Experimental results show that the hybrid method can accelerate the convergence, rapidly gaining optimal policies even in large spaces of states, exceeding the results of classical approaches to reinforcement learning.

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Paper citation in several formats:
Guisi, D.; Ribeiro, R.; Teixeira, M.; Borges, A.; Dosciatti, E. and Enembreck, F. (2016). A Hybrid Interaction Model for Multi-Agent Reinforcement Learning.In Proceedings of the 18th International Conference on Enterprise Information Systems - Volume 1: ICEIS, ISBN 978-989-758-187-8, pages 54-61. DOI: 10.5220/0005832300540061

@conference{iceis16,
author={Douglas M. Guisi. and Richardson Ribeiro. and Marcelo Teixeira. and André P. Borges. and Eden R. Dosciatti. and Fabrício Enembreck.},
title={A Hybrid Interaction Model for Multi-Agent Reinforcement Learning},
booktitle={Proceedings of the 18th International Conference on Enterprise Information Systems - Volume 1: ICEIS,},
year={2016},
pages={54-61},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005832300540061},
isbn={978-989-758-187-8},
}

TY - CONF

JO - Proceedings of the 18th International Conference on Enterprise Information Systems - Volume 1: ICEIS,
TI - A Hybrid Interaction Model for Multi-Agent Reinforcement Learning
SN - 978-989-758-187-8
AU - Guisi, D.
AU - Ribeiro, R.
AU - Teixeira, M.
AU - Borges, A.
AU - Dosciatti, E.
AU - Enembreck, F.
PY - 2016
SP - 54
EP - 61
DO - 10.5220/0005832300540061

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