Amanda Prorok, University of Cambridge
Abstract: Effective communication is key to successful, decentralized, multi-agent coordination. Yet, it is far from obvious what information is crucial to the task at hand, and how and when it must be shared among agents. In this talk, I discuss our recent work on using Graph Neural Networks (GNNs) to solve multi-agent coordination problems. In my first case-study, I show how we use GNNs to find a decentralized solution to the multi-agent path finding problem, which is known to be NP-hard. I demonstrate how our GNN-based policy is able to achieve near-optimal performance, at a fraction of the real-time computational cost. Secondly, I show how GNN-based reinforcement learning can be leveraged to learn inter-agent communication policies. In this case-study, I demonstrate how non-shared optimization objectives can lead to adversarial communication strategies. Finally, I address the challenge of learning policies for autonomous agents operating in a shared physical workspace, where the absence of collisions cannot be guaranteed. I conclude the talk by presenting a multi-vehicle mixed reality framework that facilitates the process of safely learning multi-agent navigation behaviors.