Long-Term Planning with Deep Reinforcement Learning on Autonomous Drones
This work addresses the problem of autonomous drone navigation for racing applications, representing an incremental improvement by applying existing methods to a specific domain.
The paper tackles long-term planning for autonomous drones in a racing scenario, demonstrating that a reinforcement learning agent trained with PPO outperforms a classical path planning algorithm by successfully competing against it in simulated drone races.
In this paper, we study a long-term planning scenario that is based on drone racing competitions held in real life. We conducted this experiment on a framework created for "Game of Drones: Drone Racing Competition" at NeurIPS 2019. The racing environment was created using Microsoft's AirSim Drone Racing Lab. A reinforcement learning agent, a simulated quadrotor in our case, has trained with the Policy Proximal Optimization(PPO) algorithm was able to successfully compete against another simulated quadrotor that was running a classical path planning algorithm. Agent observations consist of data from IMU sensors, GPS coordinates of drone obtained through simulation and opponent drone GPS information. Using opponent drone GPS information during training helps dealing with complex state spaces, serving as expert guidance allows for efficient and stable training process. All experiments performed in this paper can be found and reproduced with code at our GitHub repository