Using a high Update-To-Data (UTD) ratio, model-based methods have recently achieved
much higher sample efficiency than previous model-free methods for continuous-action DRL …

Abstract Mean-Field Game (MFG) serves as a crucial mathematical framework in modeling
the collective behavior of individual agents interacting stochastically with a large population …

The steep rise in reinforcement learning (RL) in various applications in energy as well as the
penetration of home automation in recent years are the motivation for this article. It surveys …

We propose VRL3, a powerful data-driven framework with a simple design for solving
challenging visual deep reinforcement learning (DRL) tasks. We analyze a number of major …

Research into algorithmic trading using reinforcement learning has been garnering
increasing popularity in recent years. While most research work focuses on solving a certain …

Deep deterministic policy gradient (DDPG) is a powerful reinforcement learning algorithm for
large-scale continuous controls. DDPG runs the back-propagation from the state-action …

The growing demand for virtual reality (VR) applications requires wireless systems to
provide a high transmission rate to support 360-degree video streaming to multiple users …

Meal delivery is a fast-growing industry supported by couriers participating in the gig
economy. This paper takes a single courier's perspective and provides decision support for …

This paper presents a novel adaptive dynamic programming (ADP) method to solve the
optimal consensus problem for a class of discrete‐time multi‐agent systems with completely …

Decision making in stochastic dynamic systems is significantly different from decision
making in deterministic systems in that agents need to make multiple management or …