深層強化学習によるクアッドロータ群の分散制御
Decentralized Control of Quadrotor Swarms with End-to-end Deep Reinforcement Learning
大規模マルチエージェント強化学習でドローン群の分散制御ポリシーを学習し、シミュレーションから実機へゼロショット転移できることを示した論文。
著者: Sumeet Batra, Zhehui Huang, Aleksei Petrenko, Tushar Kumar, Artem Molchanov, Gaurav S. Sukhatme
分類: cs.RO
原文アブストラクト
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.
