mlsanityData to 14 Oct 2026

Locally Interdependent Multi-Agent MDP: Theoretical Framework for Decentralized Agents with Dynamic Dependencies

arXiv 2406.06823 ↗First posted 10 Jun 2024cs.LG cs.AI cs.MAcited by 6

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Abstract (arXiv)

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.

Key facts. Cited by 6 papers in the map (data through 14 Oct 2026). Builds on 20 of its 38 references that are in the map. First posted on arXiv 10 Jun 2024.

Builds on

20 of 38 references.

References · 20

  1. 2017Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environmentscited by 1,558
  2. 2013The Complexity of Decentralized Control of Markov Decision Processescited by 231
  3. 2018Mean Field Multi-Agent Reinforcement Learningcited by 207
  4. 2018Fully Decentralized Multi-Agent Reinforcement Learning with Networked Agentscited by 194
  5. 2021V-Learning -- A Simple, Efficient, Decentralized Algorithm for Multiagent RLcited by 114
  6. 2020Survey of Deep Reinforcement Learning for Motion Planning of Autonomous Vehiclescited by 110
  7. 2020Near-Optimal Reinforcement Learning with Self-Playcited by 109
  8. 2020Learning Zero-Sum Simultaneous-Move Markov Games Using Function Approximation and Correlated Equilibriumcited by 100
  9. 2017Towards Optimally Decentralized Multi-Robot Collision Avoidance via Deep Reinforcement Learningcited by 83
  10. 2019Model-Free Mean-Field Reinforcement Learning: Mean-Field MDP and Mean-Field Q-Learningcited by 59
  11. 2019Multi-Agent Connected Autonomous Driving using Deep Reinforcement Learningcited by 53
  12. 2020Scalable Multi-Agent Reinforcement Learning for Networked Systems with Average Rewardcited by 50
8 more
  1. 2020Mean-Field Controls with Q-learning for Cooperative MARL: Convergence and Complexity Analysiscited by 49
  2. 2021The Power of Exploiter: Provable Multi-Agent RL in Large State Spacescited by 48
  3. 2021Towards General Function Approximation in Zero-Sum Markov Gamescited by 43
  4. 2020Multi-Agent Reinforcement Learning in Stochastic Networked Systemscited by 32
  5. 2023Breaking the Curse of Multiagency: Provably Efficient Decentralized Multi-Agent RL with Function Approximationcited by 30
  6. 2019Scalable Reinforcement Learning for Multi-Agent Networked Systemscited by 24
  7. 2019A Multi-Agent Off-Policy Actor-Critic Algorithm for Distributed Reinforcement Learningcited by 21
  8. 2021Decentralized Control of Quadrotor Swarms with End-to-end Deep Reinforcement Learningcited by 19

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 2; 2025: 0; 2026: 4.

6 papers.