Multi-Agent Pattern Formation with Deep Reinforcement Learning (Student Abstract)

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.

Authors

• Elhadji Amadou Oury Diallo
• Toshiharu Sugawara

Keywords

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