IEEE

Quantum Multi-Agent Reinforcement Learning is All You Need: Coordinated Global Access in Integrated TN/NTN Cube-Satellite Networks

Abstract: This article addresses novel quantum multi-agent reinforcement learning (QMARL)-based scheduling for integrated terrestrial ground-stations and large-scale non-terrestrial cube-satellites ...
IEEE

A Review of Safe Reinforcement Learning Methods for Modern Power Systems

Abstract: Given the availability of more comprehensive measurement data in modern power systems, reinforcement learning (RL) has gained significant interest in ...
Frontiers

Solving robotics tasks with prior demonstration via exploration-efficient deep reinforcement learning

This paper proposes an exploration-efficient deep reinforcement learning with reference (DRLR) policy framework for learning robotics tasks incorporating demonstrations. The DRLR framework is ...