Online scheduling of image satellites based on neural networks and deep reinforcement learning

Abstract In the “Internet Plus” era, space-based information services require effective and fast image satellite scheduling. Most existing studies consider image satellite scheduling to be an optimization problem to solve with searching algorithms in a batch-wise manner. No real-time speed method for satellite scheduling exists. In this paper, with the idea of building a real-time speed method, satellite scheduling is remodeled based on a Dynamic and Stochastic Knapsack Problem (DSKP), and the objective is to maximize the total expected profit. No existing algorithm could be able to solve this novel scheduling problem properly. With inspiration from the recent achievements in Deep Reinforcement Learning (DRL) in video games, AlphaGo and dynamic controlling, a novel DRL-based method is applied to training a neural network to schedule tasks. The numerical results show that the method proposed in this paper can achieve relatively good performance with real-time speed and immediate respond style.