fort was originally motivated by the desire to apply reinforcement learning methods to problems of adaptive control of queueing systems, and to the problem of adaptive routing in computer networks in particular. Environment= Dynamic system.

Operations Research Seminar, Naval Postgraduate School, Monterey, CA, February 8, 2018. slides In this article we develop techniques for applying Approximate Dynamic Programming (ADP) to the control of time-varying queuing systems. We discuss Q-learning and the integral RL algorithm as core algorithms for discrete-time (DT) and continuous-time (CT) systems, respectively. Optimal Control of Queueing Systems with Multiple Heterogeneous Facilities Rob Shone School of Mathematics Cardi University A thesis submitted for the degree of Optimal Control of Queueing Systems with Multiple Heterogeneous Facilities Rob Shone School of Mathematics Cardi University A thesis submitted for the degree of Doctor of Philosophy 2014. Reinforcement Learning for Optimal Feedback Control, 17-42.

We apply the new algorithms to the well-known problem of routing to two heterogeneous servers [7]. Further, we proposed a clustering-based technique to make the state-space finite which is critical for a tractable implementation of the RL algorithm. Index Terms— Bulk-service queueing networks, dynamic pro-gramming, Markov decision problems, optimal control, opti-mization problems, queueing theory, thresholds, transportation models. Delay-Optimal Traffic Engineering through Multi-agent Reinforcement Learning Pinyarash Pinyoanuntapong, Minwoo Lee, Pu Wang Department of Computer Science ... performance in complex networking systems with high-level uncertainties and randomness, (2) it is designed to handle Moreover, we discuss a new direction of off-policy RL for both CT and DT systems. A number of reinforcement learning algorithms have been developed recently for the solution of Markov Decision Problems, based on the ideas of asynchronous dynamic programming and stochastic approximation. INTRODUCTION ELEVATOR systems form a class of discrete-event sys-tems (DES’s) whose complexity makes them difficult to model, analyze, and optimize. In particular, we consider using model-based reinforcement learning (RL) to learn the optimal control policy of queueing networks so that the average job delay (or equivalently the average queue backlog) is minimized. CiteSeerX - Document Details (Isaac Councill, Lee Giles, Pradeep Teregowda): Semi-Markov Decision Problems are continuous time generalizations of discrete time Markov Decision Problems.

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