Reinforcement learning (RL) for partially observable Markov decision processes (POMDPs) is a challenging problem because decisions need to be made based on t...

In this paper, we investigate the problem of system identification for autonomous Markov jump linear systems (MJS) with complete state observations. We prop...

We consider the problem of scheduling maintenance for a collection of machines under partial observations when the state of each machine deteriorates stochas...

Restless bandits are a class of sequential resource allocation problems concerned with allocating one or more resources among several alternative processes...

Multi-agent reinforcement learning has made significant progress in recent years, but it remains a hard problem. Hence, one often resorts to developing lea...

In this paper, we present an online reinforcement learning algorithm, called Renewal Monte Carlo (RMC), for infinite horizon Markov decision processes with ...

We consider a static team problem in which agents observe correlated Gaussian observations and seek to minimize a quadratic cost. It is assumed that the ob...

In this paper we consider an interactive communication system with two users, who sequentially observe two correlated sources, and send the quantized observa...

In smart-metered systems, fine-grained power demand data (load profile) is communicated from a user to the utility provider. The correlation of the load pr...

In this paper, we investigate team optimal control of a population of heterogeneous LQ (Linear Quadratic) agents. The population consists of finite distinct...

Decentralized sequential hypothesis testing refers to a generalization of Wald's sequential hypothesis testing setup in which multiple decision makers make ...

The fundamental limits of remote estimation of Markov processes under communication constraints are presented. The remote estimation system consists of a sen...

The problem of optimal real-time transmission of a Markov source under constraints on the expected number of transmissions is considered, both for the discou...

Decentralized stochastic control refers to the multi-stage optimization of a dynamical system by multiple controllers that have access to different informati...

In decentralized control systems with linear dynamics, quadratic cost, and Gaussian disturbance (also called decentralized LQG systems) linear control strate...