Causality-Aware Efficient Exploration for Cooperative Multi-Agent Reinforcement Learning
Hongye Cao, Tianpei Yang, Fan Feng, Hammadi Rafik Ouariachi, Yali Du, Meng Fang, Jing Huo, Yang Gao
Abstract
Exploration is critical for cooperative multi agent reinforcement learning (MARL) to improve sample efficiency. However, existing intrinsic motivation based exploration strategies in MARL overlook the causal relationships among agents, global states, and rewards, suffering from interference by irrelevant factors and resulting in sample inefficiency. To address this issue, we propose Causality aware Efficient Exploration (CEE), a novel framework that enhances sample efficiency by inferring causal relationships between agents, global states with respect to rewards, thereby enabling causality guided exploration. Specifically, CEE operates through two components. First, CEE identifies causal relationships between global states and rewards, filtering out causally irrelevant state features that do not have a high impact on rewards to keep decision critical state information. Second, CEE discovers causal relationships between agents
BibTeX
@inproceedings{aaai2026_causalityawareef,
title = {Causality-Aware Efficient Exploration for Cooperative Multi-Agent Reinforcement Learning},
author = {Hongye Cao and Tianpei Yang and Fan Feng and Hammadi Rafik Ouariachi and Yali Du and Meng Fang and Jing Huo and Yang Gao},
booktitle = {AAAI 2026},
year = {2026}
}