Reinforcement Learning based Dynamic Task Scheduling for Multifunction Radar Network

In this paper, in order to improve the efficiency of tasks execution of multifunction radar network, a dynamic tasks scheduling problem is investigated. Considering the uncertainty of dynamic tasks request times, a dynamic tasks method that based on reinforcement learning is proposed. Firstly, we construct a Markov Decision Process (MDP) for the multifunction radar network executing tasks, and choose the dropped ratio of tasks as the evaluation criterion. Then, a model-free reinforcement learning framework for multifunction radar network executing tasks is formulated. Under the framework, we design the action space for this reinforcement learning question, and a method of tasks scheduling based on Q-learning is proposed. Finally, simulation results are provided to verify the validity of proposed method.