Inferring Latent Temporal Sparse Coordination Graph for Multi-Agent Reinforcement Learning

[2403.19253] Inferring Latent Temporal Sparse Coordination Graph for Multi-Agent Reinforcement Learning Skip to main content Search Submit Donate Log in Search arXiv Press Enter to search · Advanced search Computer Science > Machine Learning arXiv:2403.19253 (cs) [Submitted on 28 Mar 2024 ( v1 ), last revised 10 Apr 2026 (this version, v3)] Title: Inferring Latent Temporal Sparse Coordination Graph for Multi-Agent Reinforcement Learning Authors: Wei Duan , Jie Lu , Junyu Xuan View a PDF of the paper titled Inferring Latent Temporal Sparse Coordination Graph for Multi-Agent Reinforcement Learning, by Wei Duan and 2 other authors View PDF HTML (experimental) Abstract: Effective agent coordination is crucial in cooperative Multi-Agent Reinforcement Learning (MARL). While agent cooperation can be represented by graph structures, prevailing graph learning methods in MARL are limited. They rely solely on one-step observations, neglecting crucial historical experiences, leading to deficient graphs that foster redundant or detrimental information exchanges. Additionally, high computational demands for action-pair calculations in dense graphs impede scalability. To address these challenges, we propose inferring a Latent Temporal Sparse Coordination Graph (LTS-CG) for MARL. The LTS-CG leverages agents' historical observations to calculate an agent-pair probability matrix, where a sparse graph is sampled from and used for knowledge exchange between agents, thereby simultaneously capturing agent dependencies and relation uncertainty. The computational complexity of this procedure is only related to the number of agents. This graph learning process is further augmented by two innovative characteristics: Predict-Future, which enables agents to foresee upcoming observations, and Infer-Present, ensuring a thorough grasp of the environmental context from limited data. These features allow LTS-CG to construct temporal graphs from historical and real-time information, promoting knowledge exchange during policy learning and effective collaboration. Graph learning and agent training occur simultaneously in an end-to-end manner. Our demonstrated results on the StarCraft II benchmark underscore LTS-CG's superior performance. Comments: Accepted by IEEE TNNLS on 17-Nov-2024. Update Discussion Subjects: Machine Learning (cs.LG) ; Multiagent Systems (cs.MA) Cite as: arXiv:2403.19253 [cs.LG] (or arXiv:2403.19253v3 [cs.LG] for this version) https://doi.org/10.48550/arXiv.2403.19253 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Wei Duan [ view email ] [v1] Thu, 28 Mar 2024 09:20:15 UTC (979 KB) [v2] Sat, 30 Nov 2024 05:59:15 UTC (2,847 KB) [v3] Fri, 10 Apr 2026 00:20:59 UTC (2,859 KB) Full-text links: Access Paper: View a PDF of the paper titled Inferring Latent Temporal Sparse Coordination Graph for Multi-Agent Reinforcement Learning, by Wei Duan and 2 other authors View PDF HTML (experimental) TeX Source view license Current browse context: cs.LG < prev | next > new | recent | 2024-03 Change to browse by: cs cs.MA References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... 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