Deep Reinforcement Learning-based Continuous Control for Multicopter Systems

Anush Manukyan, Miguel A. Olivares-Méndez, Matthieu Geist, Holger Voos

Abstract: In this paper we apply deep reinforcement learning techniques on a multicopter for learning a stable hovering task in a continuous state action environment. We present a framework based on OpenAI GYM, Gazebo, Robotic Operating System and RotorS MAV simulator, used for successfully training different agents to perform various tasks. The deep reinforcement learning method used for the training is a model-free, on-policy, actor-critic based algorithm called Trust Region Policy Optimization (TRPO). Two neural networks have been used as nonlinear function approximators. Our experiments show that such learning approach achieves successful results, and facilitates the process of controller design.