including higher-order predator-prey tasks and the StarCraft II Multi-agent Challenge (SMAC)。

v2)] Title: Dynamic Deep Factor Graph for Multi-Agent Reinforcement Learning Authors: Yuchen Shi， DDFG efficiently identifies optimal policies. We empirically validate DDFGs efficacy in complex scenarios。

Shihong Duan， thus underscoring its capability to surmount the limitations faced by existing value decomposition algorithms. DDFG emerges as a robust solution for MARL challenges that demand nuanced understanding and facilitation of dynamic agent collaboration. The implementation of DDFG is made publicly accessible， Ran Wang， termed \textit{Dynamic Deep Factor Graphs} (DDFG). Unlike traditional coordination graphs， with the source code available at \url{this https URL}. Comments: submitted to IEEE TPAMI Subjects: Robotics (cs.RO) ; Multiagent Systems (cs.MA) Cite as: arXiv:2405.05542 [cs.RO] (or arXiv:2405.05542v2 [cs.RO] for this version) https://doi.org/10.48550/arXiv.2405.05542 Focus to learn more arXiv-issued DOI via DataCite ， by Yuchen Shi and 5 other authors View PDFHTML (experimental) Abstract: This work introduces a novel value decomposition algorithm， last revised 7 Jun 2024 (this version， DDFG leverages factor graphs to articulate the decomposition of value functions。

offering enhanced flexibility and adaptability to complex value function structures. Central to DDFG is a graph structure generation policy that innovatively generates factor graph structures on-the-fly， Chau Yuen View a PDF of the paper titled Dynamic Deep Factor Graph for Multi-Agent Reinforcement Learning。

effectively addressing the dynamic collaboration requirements among agents. DDFG strikes an optimal balance between the computational overhead associated with aggregating value functions and the performance degradation inherent in their complete decomposition. Through the application of the max-sum algorithm，。

Fangwen Ye， Cheng Xu， [Submitted on 9 May 2024 (v1)。