Learning to Lead Themselves: Agentic AI in MAS using MARL
This addresses coordination challenges in real-world deployments like drone delivery and warehouse automation, but it is incremental as it builds on existing MARL methods.
The paper tackles the problem of decentralized, cooperative decision-making in multi-agent systems, such as drone delivery, by implementing a lightweight multi-agent Proximal Policy Optimization (IPPO) approach, achieving improved task allocation and coordination without explicit communication.
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.