Recent years have witnessed significant advances in reinforcement learning (RL), which
has registered tremendous success in solving various sequential decision-making problems …

This paper is concerned with event-triggered sliding mode control (SMC) for uncertain
stochastic systems subject to limited communication capacity. We consider the stochastic …

This paper addresses the problem of learning an equilibrium efficiently in general-sum
Markov games through decentralized multi-agent reinforcement learning. Given the …

In many areas of applied mathematics, decentralization of information is a ubiquitous
attribute affecting how to approach a stochastic optimization, decision and estimation, or …

The problem of event-triggered output feedback control for networked control systems
(NCSs) with packet losses and quantization is addressed. A new dynamic quantization …

There are only a few learning algorithms applicable to stochastic dynamic teams and games
which generalize Markov decision processes to decentralized stochastic control problems …

This tutorial paper provides a comprehensive characterization of information structures in
team decision problems and their impact on the tractability of team optimization. Solution …

This work investigates the stabilization problem of uncertain stochastic Markovian jump
systems (MJSs) under communication constraints. To reduce the bandwidth usage, a …

This paper analyzes the information disclosure problems originated in economics through
the lens of information theory. Such problems are radically different from the conventional …

Sequential rate-distortion (SRD) theory provides a framework for studying the fundamental
trade-off between data-rate and data-quality in real-time communication systems. In this …