Symmetric actor–critic deep reinforcement learning for cascade quadrotor flight control

Abstract

Even though deep reinforcement learning (DRL) has been extensively applied to quadrotor flight control to simplify parameter adjustment, it has some drawbacks in terms of control performance, such as instability and asymmetry. To address these problems, we propose an odd symmetric actor to achieve stable and symmetric control performance, and an even critic to stabilize the training process. Concretely, the bias of neural networks is eliminated, and the absolute value operation is adopted to construct the activation function. Furthermore, we devise a cascade architecture, where each module trained with DRL controls a symmetric subsystem of the quadrotor. Comparative simulations have verified the effectiveness of the proposed control scheme, which shows superiority in dealing with high-dimensional, nonlinear subsystems and disadvantage in dealing with low-dimensional, linear subsystems.