Overview on game reinforcement learning methods for edge computing of low-orbit constellation

Abstract

As a new paradigm in the field of artificial intelligence, game reinforcement learning is an advanced mainstream method to solve the edge computing problem of low-orbit constellation. The multi-agent deep reinforcement learning integrated into the game perspective provides a new idea for dynamic, complex and uncertain constellation edge computing problems. By summarizing the three main research directions of satellite edge computing, namely satellite networking, task unloading and resource scheduling, the basis of game reinforcement learning paradigm is elaborated, and the typical applications in the three research directions are described respectively from the methods of game model, deep Q network, deep deterministic strategy gradient and near-end strategy optimization. In the end, the paper looks forward to the frontier challenges in this field, expected to provide a reference for the cross-fusion research of game reinforcement learning paradigm and low-orbit constellation edge computing.

Keywords

• low-orbit constellation;edge computing;game theory;multi-agent reinforcement learning