Index Terms— Bulk-service queueing networks, dynamic pro-gramming, Markov decision problems, optimal control, opti-mization problems, queueing theory, thresholds, transportation models.

We discuss Q-learning and the integral RL algorithm as core algorithms for discrete-time (DT) and continuous-time (CT) systems, respectively. In [6] we develop a new reinforcement learning method for overlay networks, where the dynamics of the underlay are unknown. 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. This research developed a reinforcement learning (RL) based control with reward functions considering energy and mobility in a joint manner-a penalty function is introduced for number of stops. However, current … First, we show that the classical state space representation in queuing systems leads to approximations that can be significantly improved by increasing the dimensionality of the state space by state disaggregation. In this article we develop techniques for applying Approximate Dynamic Programming (ADP) to the control of time-varying queuing systems. Reinforcement Learning-Based Adaptive Optimal Exponential Tracking Control of Linear Systems With Unknown Dynamics Abstract: Reinforcement learning (RL) has been successfully employed as a powerful tool in designing adaptive optimal controllers. 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. Reinforcement Learning for Optimal Feedback Control, 17-42. Mathematics and Computers in … Abstract In this paper, a novel approach based on the Q -learning algorithm is proposed to solve the infinite-horizon linear quadratic tracker (LQT) for unknown discrete-time systems in a causal manner. Self-learning (or self-play in the context of games)= Solving a DP problem using simulation-based policy iteration. Active sensory-motor systems, in addition to pro-viding for overt action, also support act:ve, selective sensing of the environment. We conclude with Finally, since many transportation systems can be modeled as multiserver batch service queueing systems, we expect our results to be useful in controlling those systems as well. Recently, off-policy learning has emerged to design optimal controllers for systems with completely unknown dynamics. This dissertation applies reinforcement learning to the adaptive control of ac-tive sensory-motor systems. Environment= Dynamic system. A ReinforcementLearning Approach to Online Web Systems Auto-configuration Xiangping Bu, Jia Rao, Cheng-Zhong Xu Department of Electrical & Computer Engineering Wayne State University, Detroit, Michigan 48202 {xpbu,jrao,czxu}@wayne.edu Abstract In a web system, configuration is crucial to the perfor-mance and service availability. Reinforcement learning is a body of theory and algorithms for optimal decision making developed within the machine learning and operations research communities in the last twenty-five years, and which have separately become important in psychology and neuroscience. Abstract This thesis discusses queueing systems in which decisions are made when customers arrive, either by individual customers themselves or by a central controller. RL methods learn the solution to optimal control and game problems online and using measured data along the system trajectories. Finally, we review several applications. (2018) The non-locality of Markov chain approximations to two-dimensional diffusions. INTRODUCTION ELEVATOR systems form a class of discrete-event sys-tems (DES’s) whose complexity makes them difficult to model, analyze, and optimize. 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 Near-optimal control of queueing systems via approximate one-step policy improvement. 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.

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