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

本文研究了基于现实生活中无人机赛事的长期规划场景，对使用 PPO 算法训练的强化学习智能体在无人机比赛中与使用传统路径规划算法的模拟无人机进行了实验，使用对手无人机的 GPS 信息作为专家指导进行训练，成功解决了复杂状态空间问题，其代码可以在我们的 GitHub 存储库中找到。