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

Mass personalization is becoming a reality. It requires responsive and flexible
manufacturing operations for producing individualized products in dynamic batch sizes at …

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 …

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 paper presents a probabilistic framework for synthesizing control policies for general
multi-robot systems that is based on decentralized partially observable Markov decision …

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” …

We consider both finite-horizon and infinite-horizon versions of a dynamic game with selfish
players who observe their types privately and take actions that are publicly observed …

We consider a networked control system consisting of a remote controller and a collection of
linear plants, each associated with a local controller. Each local controller directly observes …

This paper addresses decentralized event-triggered synchronous control for a master–slave
neural network. Firstly, a decentralized event-triggered scheme is presented for saving the …