mlsanityData to 14 Oct 2026

Learning to Lead Themselves: Agentic AI in MAS using MARL

arXiv 2510.00022 ↗First posted 24 Sep 2025cs.AI cs.MAcited by 1

On the map: LLM agents and tool use → Multimodal and embodied agents

Abstract (arXiv)

As autonomous systems move from prototypes to real deployments, the ability of multiple agents to make decentralized, cooperative decisions becomes a core requirement. This paper examines how agentic artificial intelligence, agents that act independently, adaptively and proactively can improve task allocation and coordination in multi-agent systems, with primary emphasis on drone delivery and secondary relevance to warehouse automation. We formulate the problem in a cooperative multi-agent reinforcement learning setting and implement a lightweight multi-agent Proximal Policy Optimization, called IPPO, approach in PyTorch under a centralized-training, decentralized-execution paradigm. Experiments are conducted in PettingZoo environment, where multiple homogeneous drones or agents must self-organize to cover distinct targets without explicit communication.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Placed in Multimodal and embodied agents, one of 16 directions in LLM agents and tool use. Builds on 25 of its 55 references that are in the map. First posted on arXiv 24 Sep 2025.

Builds on

25 of 55 references.

References · 25

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13 more
  1. 2019Multi-Agent Reinforcement Learning: A Selective Overview of Theories and Algorithmscited by 567
  2. 2017Value-Decomposition Networks For Cooperative Multi-Agent Learningcited by 503
  3. 2018Unity: A General Platform for Intelligent Agentscited by 236
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Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 1.

1 paper.

  1. 2026AB-RAG: Adaptive Budgeted Retrieval-Augmented Generation for Reliable Question Answeringcited by 0

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  1. 2026Intercepting an Agile Target with Net-Carrying Drones using Competitive Multi-Agent Reinforcement Learning ↗
  2. 2025Dynamic one-time delivery of critical data by small and sparse UAV swarms: a model problem for MARL scaling studies
  3. 2025An Extended Benchmarking of Multi-Agent Reinforcement Learning Algorithms in Complex Fully Cooperative Tasks
  4. 2024MARLander: A Local Path Planning for Drone Swarms using Multiagent Deep Reinforcement Learning ↗
  5. 2025IPPO Learns the Game, Not the Team: A Study on Generalization in Heterogeneous Agent Teams
  6. 2025Goal-Oriented Multi-Agent Reinforcement Learning for Decentralized Agent Teams
  7. 2026ACPO: Agent-Chained Policy Optimization for Multi-Agent Reinforcement Learning
  8. 2025MARL Warehouse Robots