Long-Term Planning with Deep Reinforcement Learning on Autonomous Drones
- URL: http://arxiv.org/abs/2007.05694v1
- Date: Sat, 11 Jul 2020 06:16:50 GMT
- Title: Long-Term Planning with Deep Reinforcement Learning on Autonomous Drones
- Authors: Ugurkan Ates
- Abstract summary: 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.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: 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
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