Multi-Agent Causal Reinforcement Learning

André Meyer-Vitali

2025

Abstract

It has become clear that mere correlations extracted from data through statistical processes are insufficient to give insight into the causal relationships inherent in them. Causal models support the necessary understanding of these relationships to make transparent and robust decisions. In a distributed setting, the causal models that are shared between agents improve their coordination and collaboration. They learn individually and from each other to optimise a system’s behaviour. We propose a combination of causal models and multi-agent reinforcement learning to create reliable and trustworthy AI systems. This combination strengthens the modelling and reasoning of agents that communicate and collaborate using shared causal insights. A comprehensive method for applying and integrating these aspects is being developed.

Paper Citation

in Harvard Style

Meyer-Vitali A. (2025). Multi-Agent Causal Reinforcement Learning. In Proceedings of the 13th International Conference on Model-Based Software and Systems Engineering - Volume 1: MBSE-AI Integration; ISBN 978-989-758-729-0, SciTePress, pages 435-442. DOI: 10.5220/0013400100003896

in Bibtex Style

@conference{mbse-ai integration25,
author={André Meyer-Vitali},
title={Multi-Agent Causal Reinforcement Learning},
booktitle={Proceedings of the 13th International Conference on Model-Based Software and Systems Engineering - Volume 1: MBSE-AI Integration},
year={2025},
pages={435-442},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013400100003896},
isbn={978-989-758-729-0},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 13th International Conference on Model-Based Software and Systems Engineering - Volume 1: MBSE-AI Integration
TI - Multi-Agent Causal Reinforcement Learning
SN - 978-989-758-729-0
AU - Meyer-Vitali A.
PY - 2025
SP - 435
EP - 442
DO - 10.5220/0013400100003896
PB - SciTePress