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Authors: Abdel Rodríguez 1 ; Ricardo Grau 2 and Ann Nowé 3

Affiliations: 1 Central University of Las Villas and Vrije Universiteit Brussel, Cuba ; 2 Central University of Las Villas, Cuba ; 3 Vrije Universiteit Brussel, Belgium

ISBN: 978-989-8425-41-6

ISSN: 2184-433X

Keyword(s): CARLA, Convergence, Performance.

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

Abstract: Reinforcement Learning is a powerful technique for agents to solve unknown Markovian Decision Processes, from the possibly delayed signals that they receive. Most RL work, in particular for multi-agent settings, assume a discrete action set. Learning automata are reinforcement learners, belonging to the category of policy iterators, that exhibit nice convergence properties in discrete action settings. Unfortunately, most applications assume continuous actions. A formulation for a continuous action reinforcement learning automaton already exists, but there is no convergence guarantee to optimal decisions. An improve of the performance of the method is proposed in this paper as well as the proof for the local convergence.

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Paper citation in several formats:
Rodríguez, A.; Grau, R. and Nowé, A. (2011). CONTINUOUS ACTION REINFORCEMENT LEARNING AUTOMATA - Performance and Convergence.In Proceedings of the 3rd International Conference on Agents and Artificial Intelligence - Volume 1: ICAART, ISBN 978-989-8425-41-6, ISSN 2184-433X, pages 473-478. DOI: 10.5220/0003287104730478

@conference{icaart11,
author={Abdel Rodríguez. and Ricardo Grau. and Ann Nowé.},
title={CONTINUOUS ACTION REINFORCEMENT LEARNING AUTOMATA - Performance and Convergence},
booktitle={Proceedings of the 3rd International Conference on Agents and Artificial Intelligence - Volume 1: ICAART,},
year={2011},
pages={473-478},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0003287104730478},
isbn={978-989-8425-41-6},
}

TY - CONF

JO - Proceedings of the 3rd International Conference on Agents and Artificial Intelligence - Volume 1: ICAART,
TI - CONTINUOUS ACTION REINFORCEMENT LEARNING AUTOMATA - Performance and Convergence
SN - 978-989-8425-41-6
AU - Rodríguez, A.
AU - Grau, R.
AU - Nowé, A.
PY - 2011
SP - 473
EP - 478
DO - 10.5220/0003287104730478

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