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Authors: Jos van de Wolfshaar ; Marco Wiering and Lambert Schomaker

Affiliation: University of Groningen, Netherlands

ISBN: 978-989-758-275-2

Keyword(s): Reinforcement Learning, Deep Learning, Learning Vector Quantization, Nearest Prototype Classification, Deep Reinforcement Learning, Actor-Critic.

Related Ontology Subjects/Areas/Topics: Artificial Intelligence ; Biomedical Engineering ; Biomedical Signal Processing ; Computational Intelligence ; Evolutionary Computing ; Health Engineering and Technology Applications ; Human-Computer Interaction ; Knowledge Discovery and Information Retrieval ; Knowledge-Based Systems ; Machine Learning ; Methodologies and Methods ; Neural Networks ; Neurocomputing ; Neurotechnology, Electronics and Informatics ; Pattern Recognition ; Physiological Computing Systems ; Sensor Networks ; Signal Processing ; Soft Computing ; Symbolic Systems ; Theory and Methods

Abstract: We introduce a novel type of actor-critic approach for deep reinforcement learning which is based on learning vector quantization. We replace the softmax operator of the policy with a more general and more flexible operator that is similar to the robust soft learning vector quantization algorithm. We compare our approach to the default A3C architecture on three Atari 2600 games and a simplistic game called Catch. We show that the proposed algorithm outperforms the softmax architecture on Catch. On the Atari games, we observe a nonunanimous pattern in terms of the best performing model.

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Paper citation in several formats:
van de Wolfshaar, J.; Wiering, M. and Schomaker, L. (2018). Deep Learning Policy Quantization.In Proceedings of the 10th International Conference on Agents and Artificial Intelligence - Volume 1: ICAART, ISBN 978-989-758-275-2, pages 122-130. DOI: 10.5220/0006592901220130

@conference{icaart18,
author={Jos van de Wolfshaar. and Marco Wiering. and Lambert Schomaker.},
title={Deep Learning Policy Quantization},
booktitle={Proceedings of the 10th International Conference on Agents and Artificial Intelligence - Volume 1: ICAART,},
year={2018},
pages={122-130},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006592901220130},
isbn={978-989-758-275-2},
}

TY - CONF

JO - Proceedings of the 10th International Conference on Agents and Artificial Intelligence - Volume 1: ICAART,
TI - Deep Learning Policy Quantization
SN - 978-989-758-275-2
AU - van de Wolfshaar, J.
AU - Wiering, M.
AU - Schomaker, L.
PY - 2018
SP - 122
EP - 130
DO - 10.5220/0006592901220130

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