Turn-Based Multi-Agent Reinforcement Learning Model Checking

Dennis Gross

2023

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.

Paper Citation

in Harvard Style

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, pages 980-987. DOI: 10.5220/0011872800003393

in Bibtex Style

@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},
}

in EndNote Style

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
AU - Gross D.
PY - 2023
SP - 980
EP - 987
DO - 10.5220/0011872800003393