Aditya Mahajan

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

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

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

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

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

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

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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...

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

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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...

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

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

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

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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...

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

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

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