An Algorithm of Reinforcement Learning for Maneuvering Parameter Self-Tuning Applying in Satellite Cluster

Abstract

Satellite cluster is a type of artificial cluster, which is attracting wide attention at present. Although the traditional empirical parameter method (TEPM) has the potential to deal with the mission of satellite flocking, it is difficult to select the proper parameters. In order to improve the flight effect in the problem of satellite cluster, as well as to make the selection of flight parameters more reasonable, the traditional sensing zones are improved. A 3σ position error ellipsoid and an induction ellipsoid are applied for substituting the traditional repulsing zone and attracting zone, respectively. Besides, we propose an algorithm of reinforcement learning for parameter self-tuning (RLPST), which is based on the actor-critic framework, to automatically learn the suitable flight parameters. To obtain the parameters in the repulsing zone, orientating zone, and attracting zone of each member in the cluster, a three-channel learning framework is designed. The learning process makes the framework finally find the suitable parameters. Numerical experimental results have shown the superiorities compared to the traditional method, which include trajectory deviation and sensing rate or terminal matching rate, as well as the improvement of the flight paths under the learning framework.

Accelerating Fuzzy Actor–Critic Learning via Suboptimal Knowledge for a Multi-Agent Tracking Problem

“…Through employing the policy of centralized training with decentralized execution, an improved MADDPG was proposed to evaluate the value function more accurately in a UAV cluster [36]. To allow for real applications of the multi-agent RL technique, a timing recovery loop for PSK and QAM modulations based on swarm reinforcement learning were proposed for high-speed telecommunications systems [37,38].…”

An algorithm of pretrained fuzzy actor–critic learning applying in fixed-time space differential game

“…In recent years, the actor-critic algorithm has been attempted to solve some typical differential games under the unknown environment. [28][29][30][31] One of the typical games is the problem of territory guarding, which is a type of grid walking game on the ground. 32 In addition, the differential game between the pursuer and the evader with the single control input separately has been considered in References.…”