让智能体自主协作完成无人机配送任务，提升系统效率。
Learning to Lead Themselves: Agentic AI in MAS using MARL
- 采用轻量级分布式强化学习框架，实现无通信下的自主协调
- 在PettingZoo中使多架同质无人机高效覆盖目标区域
- 适合研究自主协同系统或无人机调度的开发者参考
随着自主系统从原型走向实际部署，多个智能体在去中心化环境下进行协作决策的能力成为核心需求。本文研究具备自主性、自适应性和主动性特征的智能体如何提升多智能体系统中的任务分配与协调效率，重点应用于无人机配送，次要涉及仓库自动化。问题被建模为合作式多智能体强化学习任务，在集中训练、分散执行范式下，使用PyTorch实现了轻量级多智能体近端策略优化（IPPO）方法。实验在PettingZoo环境中开展，多个同质无人机需在无显式通信条件下自我组织以覆盖不同目标。
原文摘要 · Abstract (English)
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.
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