Decentralized Control of Quadrotor Swarms with End-to-end Deep Reinforcement Learning
This enables robust and scalable autonomous swarm control for applications like surveillance or delivery, though it builds incrementally on existing deep RL methods.
The paper tackled the problem of controlling drone swarms with decentralized policies learned via reinforcement learning, achieving zero-shot transfer to real quadrotors for tasks like flocking and obstacle avoidance.
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.