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

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 fu...

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Bibliographic Details
Published in:arXiv.org 2021-11
Main Authors: Batra, Sumeet, Huang, Zhehui, Petrenko, Aleksei, Kumar, Tushar, Molchanov, Artem, Sukhatme, Gaurav S
Format: Article
Language:English
Subjects:
Collisions
Decentralized control
Deep learning
Maneuvers
Moving obstacles
Multiagent systems
Neural networks
Policies
Rotary wing aircraft
Simulation
Stationkeeping
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Summary: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. Code and video demonstrations are available on the project website at https://sites.google.com/view/swarm-rl.
ISSN:2331-8422