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

Multi-agent reinforcement learning (MARL) has long been a significant research topic in
both machine learning and control systems. Recent development of (single-agent) deep …

Our goal in writing this book has been to provide a comprehensive, mathematically rigorous,
but still accessible treatment of the interaction between information and control in multi …

A general model of decentralized stochastic control called partial history sharing information
structure is presented. In this model, at each step the controllers share part of their …

We propose a theoretical framework for approximate planning and learning in partially
observed systems. Our framework is based on the fundamental notion of information state …

Consider a first order linear time-invariant discrete time system driven by process noise, a
pre-processor that accepts causal measurements of the state of the system, and a state …

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

In this article, we consider sequential dynamic team decision problems with nonclassical
information structures. First, we address the problem from the point of view of a “manager” …

Most cyber–physical systems (CPS) encounter a large volume of data which is added to the
system gradually in real time and not altogether in advance. In this paper, we provide a …

Subsystems that are coupled due to dynamics and costs arise naturally in various
communication applications. In many such applications the control actions are shared …