Locally Interdependent Multi-Agent MDP: Theoretical Framework for Decentralized Agents with Dynamic Dependencies
Alex DeWeese, Guannan Qu
Abstract
Many multi-agent systems in practice are decentralized and have dynamically varying dependencies. There has been a lack of attempts in the literature to analyze these systems theoretically. In this paper, we propose and theoretically analyze a decentralized model with dynamically varying dependencies called the Locally Interdependent Multi-Agent MDP. This model can represent problems in many disparate domains such as cooperative navigation, obstacle avoidance, and formation control. Despite the intractability that general partially observable multi-agent systems suffer from, we propose three closed-form policies that are theoretically near-optimal in this setting and can be scalable to compute and store. Consequentially, we reveal a fundamental property of Locally Interdependent Multi-Agent MDP's that the partially observable decentralized solution is exponentially close to the fully observable solution with respect to the visibility radius. We then discuss extensions of our closed-form policies to further improve tractability. We conclude by providing simulations to investigate some long horizon behaviors of our closed-form policies.
Ask about this paper
Your agent reads all of it.
Lune indexed this paper to the last equation, along with the top-tier papers that cite it. Ask a question and the answer quotes them.
Your agent calls
Luneget_paper_fulltext
Free to start. No credit card required.
Terminal
Install the CLIlune papers fulltext 459ebc0b-d35c-4771-9a6b-84dd4de6dbe8Cited by top-tier papers4
- Mean-Field Sampling for Cooperative Multi-Agent Reinforcement LearningEmile Anand, Ishani Karmarkar, Guannan QuNeurIPS 2025 · 10 citations
- Exploiting Structure in Offline Multi-Agent RL: The Benefits of Low Interaction RankWenhao Zhan, Scott Fujimoto, Zheqing Zhu, Jason D. Lee et al.ICLR 2025
- Distilling Task-Level Coordination Policies for Generalizable Multi-Agent CooperationZimo Zhai, Manjie Xu, Wei LiangICML 2026
- INS: Interaction-aware Synthesis to Enhance Offline Multi-agent Reinforcement LearningYuqian Fu, Yuanheng Zhu, Jian Zhao, Jiajun Chai et al.ICLR 2025
Builds on5
- Near-Optimal Reinforcement Learning with Self-PlayYu Bai, Chi Jin, Tiancheng YuNeurIPS 2020 · 150 citations
- Scalable Multi-Agent Reinforcement Learning for Networked Systems with Average RewardGuannan Qu, Yiheng Lin, Adam Wierman, Na LiNeurIPS 2020 · 99 citations
- The Power of Exploiter: Provable Multi-Agent RL in Large State SpacesChi Jin, Qinghua Liu, Tiancheng YuICML 2022 · 59 citations
- Multi-Agent Reinforcement Learning in Stochastic Networked SystemsYiheng Lin, Guannan Qu, Longbo Huang, Adam WiermanNeurIPS 2021 · 55 citations
- Towards General Function Approximation in Zero-Sum Markov GamesBaihe Huang, Jason D. Lee, Zhaoran Wang, Zhuoran YangICLR 2022 · 50 citations
Related papers
- Local Policies for Graph-Structured Markov Decision ProcessesFathima Faizal, Asuman Ozdaglar, Martin WainwrightICML 2026
- Efficient Multiagent Planning via Shared Action SuggestionsDylan M. Asmar, Mykel J. KochenderferAAAI 2026
- Multi-agent active perception with prediction rewardsMikko Lauri, Frans A. OliehoekNeurIPS 2020 · 13 citations
- Shield Decentralization for Safe Multi-Agent Reinforcement LearningDaniel Melcer, Christopher Amato, Stavros TripakisNeurIPS 2022 · 26 citations
- Decentralized Stochastic Multi-Player Multi-Armed Walking BanditsGuojun Xiong, Jian LiAAAI 2023 · 2 citations
