Fixed-Wing UAVs flocking in continuous spaces: A deep reinforcement learning approach

Abstract Fixed-Wing UAVs (Unmanned Aerial Vehicles) flocking is still a challenging problem due to the kinematics complexity and environmental dynamics. In this paper, we solve the leader–followers flocking problem using a novel deep reinforcement learning algorithm that can generate roll angle and velocity commands by training an end-to-end controller in continuous state and action spaces. Specifically, we choose CACLA (Continuous Actor–Critic Learning Automation) as the base algorithm and we use the multi-layer perceptron to represent both the actor and the critic. Besides, we further improve the learning efficiency by using the experience replay technique that stores the training data in the experience memory and samples from the memory as needed. We have compared the performance of the proposed CACER (Continuous Actor–Critic with Experience Replay) algorithm with benchmark algorithms such as DDPG and double DQN in numerical simulation, and we have demonstrated the performance of the learned optimal policy in semi-physical simulation without any parameter tuning.