A Dyna-Q (Lambda) Approach to Flocking with Fixed-Wing UAVs in a Stochastic Environment

Abstract

Unmanned Aerial Vehicles (UAVs) have demonstrated their efficacy in supporting both military and civilian applications, many of which contain tasks that are parallel in nature, and can benefit from cooperation in terms of effectiveness. One of the fundamental challenges of multi-UAV systems is autonomous team coordination. This paper looks at flocking with small fixed-wing UAVs in the context of a model-free reinforcement learning problem. Dyna-Q ( ) with a variable learning rate is employed by the agents to learn a control policy that facilitates flocking in a leader-follower topology while operating in a stochastic environment. Simulation results demonstrate the followers learning and adapting their policies to non-stationary stochastic environments.