Decentralized Control of Quadrotor Swarms with End-to-end Deep Reinforcement LearningDownload PDF

Published: 13 Sept 2021, Last Modified: 05 May 2023CoRL2021 PosterReaders: Everyone
Keywords: Deep Reinforcement Learning, Robot Learning, Multi-Agent Systems, Quadrotors
Abstract: We demonstrate the possibility of learning drone swarm controllers that are zero-shot transferable to real quadrotors via large-scale multi-agent end-to-end reinforcement learning. We train policies parameterized by neural networks that are capable of controlling individual drones in a swarm in a fully decentralized manner. Our policies, trained in simulated environments with realistic quadrotor physics, demonstrate advanced flocking behaviors, perform aggressive maneuvers in tight formations while avoiding collisions with each other, break and re-establish formations to avoid collisions with moving obstacles, and efficiently coordinate in pursuit-evasion tasks. We analyze, in simulation, how different model architectures and parameters of the training regime influence the final performance of neural swarms. We demonstrate the successful deployment of the model learned in simulation to highly resource-constrained physical quadrotors performing station keeping and goal swapping behaviors. Video demonstrations and source code are available at the project website
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