Situation-Dependent Causal Influence-Based Cooperative Multi-agent Reinforcement Learning

• URL: http://arxiv.org/abs/2312.09539v1
• Date: Fri, 15 Dec 2023 05:09:32 GMT
• Title: Situation-Dependent Causal Influence-Based Cooperative Multi-agent Reinforcement Learning
• Authors: Xiao Du, Yutong Ye, Pengyu Zhang, Yaning Yang, Mingsong Chen, Ting Wang
• Abstract summary: We propose a novel MARL algorithm named Situation-Dependent Causal Influence-Based Cooperative Multi-agent Reinforcement Learning (SCIC) Our approach aims to detect inter-agent causal influences in specific situations based on the criterion using causal intervention and conditional mutual information. The resulting update links coordinated exploration and intrinsic reward distribution, which enhance overall collaboration and performance.
• Score: 18.054709749075194
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Learning to collaborate has witnessed significant progress in multi-agent reinforcement learning (MARL). However, promoting coordination among agents and enhancing exploration capabilities remain challenges. In multi-agent environments, interactions between agents are limited in specific situations. Effective collaboration between agents thus requires a nuanced understanding of when and how agents' actions influence others. To this end, in this paper, we propose a novel MARL algorithm named Situation-Dependent Causal Influence-Based Cooperative Multi-agent Reinforcement Learning (SCIC), which incorporates a novel Intrinsic reward mechanism based on a new cooperation criterion measured by situation-dependent causal influence among agents. Our approach aims to detect inter-agent causal influences in specific situations based on the criterion using causal intervention and conditional mutual information. This effectively assists agents in exploring states that can positively impact other agents, thus promoting cooperation between agents. The resulting update links coordinated exploration and intrinsic reward distribution, which enhance overall collaboration and performance. Experimental results on various MARL benchmarks demonstrate the superiority of our method compared to state-of-the-art approaches.

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