Multi-Agent Pattern Formation with Deep Reinforcement Learning (Student Abstract) - Diallo, Elhadji Amadou Oury; Sugawara, Toshiharu (Proceedings of the AAAI Conference on Artificial Intelligence, 2020)
We propose a decentralized multi-agent deep reinforcement learning architecture to investigate pattern formation under the local information provided by the agents’ sensors. It consists of tasking a large number of homogeneous agents to move to a set of specified goal locations, addressing both the assignment and trajectory planning sub-problems concurrently. We then show that agents trained on random patterns can organize themselves into very complex shapes.