Scalable Reinforcement Learning Policies for Multi-Agent Control
Christopher D. Hsu,Heejin Jeong,George J. Pappas,Pratik Chaudhari,Christopher D. Hsu,Heejin Jeong,George J. Pappas,Pratik Chaudhari
We develop a Multi-Agent Reinforcement Learning (MARL) method to learn scalable control policies for target tracking. Our method can handle an arbitrary number of pursuers and targets; we show results for tasks consisting up to 1000 pursuers tracking 1000 targets. We use a decentralized, partially-observable Markov Decision Process framework to model pursuers as agents receiving partial observatio...
