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Authors: Jingyi Huang 1 ; Fabio Giardina 2 and Andre Rosendo 1

Affiliations: 1 School of Information Science and Technology, ShanghaiTech University, China ; 2 John A. Paulson School of Engineering and Applied Sciences, Harvard, U.S.A.

Keyword(s): Reinforcement Learning, Policy Search, Robotics.

Abstract: Deep Learning experiments commonly require hundreds of trials to properly train neural networks, often labeled as Big Data, while Bayesian learning leverages scarce data points to infer next iterations, also known as Micro Data. Deep Bayesian Learning combines the complexity from multi-layered neural networks to probabilistic inferences, and it allows a robot to learn good policies within few trials in the real world. In here we propose, for the first time, an application of Deep Bayesian Reinforcement Learning (RL) on a real-world multi-robot confrontation game, and compare the algorithm with a model-free Deep RL algorithm, Deep Q-Learning. Our experiments show that DBRL significantly outperforms DRL in learning efficiency and scalability. The results of this work point to the advantages of Deep Bayesian approaches in bypassing the Reality Gap and sim-to-real implementations, as the time taken for real-world learning can quickly outperform data-intensive Deep alternatives.

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Paper citation in several formats:
Huang, J., Giardina, F. and Rosendo, A. (2021). Deep vs. Deep Bayesian: Faster Reinforcement Learning on a Multi-robot Competitive Experiment. In Proceedings of the 18th International Conference on Informatics in Control, Automation and Robotics - ICINCO; ISBN 978-989-758-522-7; ISSN 2184-2809, SciTePress, pages 501-506. DOI: 10.5220/0010601905010506

@conference{icinco21,
author={Jingyi Huang and Fabio Giardina and Andre Rosendo},
title={Deep vs. Deep Bayesian: Faster Reinforcement Learning on a Multi-robot Competitive Experiment},
booktitle={Proceedings of the 18th International Conference on Informatics in Control, Automation and Robotics - ICINCO},
year={2021},
pages={501-506},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010601905010506},
isbn={978-989-758-522-7},
issn={2184-2809},
}

TY - CONF

JO - Proceedings of the 18th International Conference on Informatics in Control, Automation and Robotics - ICINCO
TI - Deep vs. Deep Bayesian: Faster Reinforcement Learning on a Multi-robot Competitive Experiment
SN - 978-989-758-522-7
IS - 2184-2809
AU - Huang, J.
AU - Giardina, F.
AU - Rosendo, A.
PY - 2021
SP - 501
EP - 506
DO - 10.5220/0010601905010506
PB - SciTePress

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