Turn-Based Multi-Agent Reinforcement Learning Model Checking

Dennis Gross

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Paper

Turn-Based Multi-Agent Reinforcement Learning Model Checking

Topics: Autonomous Systems; Deep Learning; Machine Learning; Model-Based Reasoning; Multi-Agent Systems; Neural Networks; Planning and Scheduling; Uncertainty in AI

In Proceedings of the 15th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART, 980-987, 2023 , Lisbon, Portugal

Author: Dennis Gross

Affiliation: Institute for Computing and Information Sciences, Radboud University, Toernooiveld 212, 6525 EC Nijmegen, The Netherlands

Keyword(s): Turn-Based Multi-Agent Reinforcement Learning, Model Checking.

Abstract: In this paper, we propose a novel approach for verifying the compliance of turn-based multi-agent reinforcement learning (TMARL) agents with complex requirements in stochastic multiplayer games. Our method overcomes the limitations of existing verification approaches, which are inadequate for dealing with TMARL agents and not scalable to large games with multiple agents. Our approach relies on tight integration of TMARL and a verification technique referred to as model checking. We demonstrate the effectiveness and scalability of our technique through experiments in different types of environments. Our experiments show that our method is suited to verify TMARL agents and scales better than naive monolithic model checking.

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Paper citation in several formats:

Gross, D. (2023). Turn-Based Multi-Agent Reinforcement Learning Model Checking. In Proceedings of the 15th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART; ISBN 978-989-758-623-1; ISSN 2184-433X, SciTePress, pages 980-987. DOI: 10.5220/0011872800003393

@conference{icaart23,
author={Dennis Gross},
title={Turn-Based Multi-Agent Reinforcement Learning Model Checking},
booktitle={Proceedings of the 15th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART},
year={2023},
pages={980-987},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011872800003393},
isbn={978-989-758-623-1},
issn={2184-433X},
}

TY - CONF

JO - Proceedings of the 15th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART
TI - Turn-Based Multi-Agent Reinforcement Learning Model Checking
SN - 978-989-758-623-1
IS - 2184-433X
AU - Gross, D.
PY - 2023
SP - 980
EP - 987
DO - 10.5220/0011872800003393
PB - SciTePress