A Reusable Simulation Pipeline for Many-Agent Reinforcement Learning

Abstract

Recent advancements in multi-agent reinforcement learning led to systems in which large groups of agents work together to learn shared policies and achieve collective behavior. This approach is increasingly important for many applications, including swarm robotics, crowd sensing, and large-scale IoT networks. In fact, these systems require repeated experimentation to learn from experience: simulation becomes thus essential, as deploying and testing in real-world environments incurs in high costs and practical challenges. In response to this need, our paper introduces a simulation-based pipeline to gather the necessary experience for many-agent learning. We highlight the requirements of such pipeline and the role of simulation, presenting also a practical prototype implemented in Alchemist, a simulator designed for very large-scale systems. This pipeline provides a scalable, modular, and flexible environment for developing and testing many-agent reinforcement learning strategies.