reinforcement learning for optimal control of queueing systems

The adapted approach mimics the neural computations that allow our Interested in research on Reinforcement Learning? Finally, the theory and simulation demonstrate that the optimized formation scheme can guarantee the desired control performance. The goal of QRON is to find a QoS-satisfied overlay path, while trying to balance the overlay traffic among the OBs and the overlay links in the OSN. solution for Optimal Control that cannot be implemented by going forward in real time. and close to the state of the art for any reinforcement learning algorithm. Our controller only uses the queue length information of the network and requires no knowledge about the network topology or system parameters. Q-learning for Optimal Control of Continuous-time Systems Biao Luo, Derong Liu, Fellow, IEEE and Tingwen Huang AbstractâIn this paper, two Q-learning (QL) methods are proposed and their convergence theories are established for ad-dressing the model-free optimal control problem of general non-linear continuous-time systems. combines the Minimax-Q algorithm and QS-algorithm. International Journal of Control: Vol. Reinforcement learning (RL) is a model-free framework for solving optimal control problems stated as Markov decision processes (MDPs) (Puterman, 1994). At the finer grain, a per-core Reinforcement Learning (RL) method is used to learn the optimal control policy of the Voltage/Frequency (VF) levels in a model-free manner. To read the full-text of this research, you can request a copy directly from the authors. We study an Reinforcement Learning and Optimal Control A Selective Overview Dimitri P. Bertsekas Laboratory for Information and Decision Systems Massachusetts Institute of Technology March 2019 Bertsekas (M.I.T.) Traditional approaches in RL, however, cannot handle the unbounded state spaces of the network control problem. In [6] we develop a new reinforcement learning method for overlay networks, where the dynamics of the underlay are unknown. We also present several results on the performance of multiclass queueing networks operating under general Markovian and, in particular, priority policies. In this paper, an event-triggered reinforcement learning-based met-hod is developed for model-based optimal synchronization control of multiple Euler-Lagrange systems (MELSs) under a directed graph. Furthermore, delay stability in this case may depend on However, finding optimal control policies 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. The method uses linear or We present a new algorithm, Prioritized Sweeping, for e cient prediction and control of stochas-tic Markov systems. <>>>/Filter/FlateDecode/Length 19>> Here we demonstrate that gliding and landing strategies with different optimality criteria can be identified through deep reinforcement learning without explicit knowledge of the underlying physics. In this work, we consider using model-based reinforcement learning (RL) to learn the optimal control policy for queueing networks so that the average job delay (or â¦ A reinforcement learningâbased scheme for direct adaptive optimal control of linear stochastic systems Wee Chin Wong School of Chemical and Biomolecular Engineering, Georgia Institute of Technology, Atlanta, GA 30332, U.S.A. After each time slot, each user that has transmitted a packet receives a local observation indicating whether its packet was successfully delivered or not (i.e., ACK signal). Unfortunately, this has rarely been addressed in current research. The overlay nodes are capable of implementing any dynamic routing policy, however, the legacy underlay has a fixed, single path routing scheme and uses a simple work-conserving forwarding policy. Several recent studies realized that a measurable number of path outages were unavoidable even with use of such overlay networks. The journal is primarily interested in probabilistic and statistical problems in this setting. endstream Finally, it describes the high level architecture of the overlays. Most provably-efficient learning algorithms introduce optimism about Our analysis results show that a single-hop overlay path provides the same degree of path diversity as the multi-hop overlay path for more than 90% of source and destination pairs. then follows the policy that is optimal for this sample during the episode. constraints that the applications should satisfy to ensure Quality of Service (QoS). queueing networks under a stable policy. Shaler Stidham, Jr. Shaler Stidham, Jr. ... Reinforcement learning models for scheduling in wireless networks. Approximate dynamic programming techniques and RL have been applied to queueing problems in prior work [30,42,37], though their settings and goals are quite different from us, and their approaches exploit prior knowledge of queueing theory and specific structures of the problems. We explore the use of minimal resource allocation neural network (mRAN), and develop a mRAN function approximation approach to RL systems. The authors in, Delay stability of back-pressure policies in the presence of heavy-tailed traffic, Geometric bounds for stationary distributions of infinite markov chains via lyapunov functions, This paper presents a detailed study of average reward reinforcement learning, an undiscounted optimality framework that is more appropriate for cyclical tasks than the much better studied discounted framework. Predictive Control for Linear and Hybrid Systems. We consider open and closed multiclass queueing networks, with Poisson arrivals (for open networks), exponentially distributed class dependent service times and class dependent deterministic or probabilistic routing. Experimental results show that this approach improves QoS significantly and efficiently. method. Currently, each of these applications requires their proprietary functionality support. At the start of each episode, PSRL updates a prior distribution We develop a programmatic procedure for establishing the stability Subsequently, a more complicated problem is considered, involving routing control in a system which consists of heterogeneous, multiple-server facilities arranged in parallel. This is due to the difficulty of analyzing regret under episode switching schedules that depend on random variables of the true underlying model. Due to the complex nature of the queueing dynamics, such a problem has no analytic solution so that previous research often resorts to heavy-traffic analysis in that both the arrival rate and service rate are sent to infinity. The obtained control â¦ Both simulation results and the field experimental results demonstrate the effectiveness of the algorithm, especially in the adaptivity to the individual tradeoff between thermal and acoustic comfort. x�+��4Pp�� Recently, overlay networks have emerged as a means to enhance end-to-end application performance and availability. We show that when K=N, there is an optimal policy which serves the queues so that the resulting vector of queue lengths is "Most Balanced" (MB). The shared bandwidth is divided into K orthogonal channels, and the users access the spectrum using a random access protocol. Offered by University of Alberta. âA Tour of Reinforcement Learning: The View from Continuous Control.â arXiv:1806.09460. By using Q-function, we propose an online learning scheme to estimate the kernel matrix of Q-function and to update the control gain using the data along the system trajectories. An overlay network's ability to quickly recover from path outages and congestion is limited unless we ensure path independence at the IP layer. Ensuring quality of service (QoS) guarantees in service systems is a challenging task, particularly when the system is composed of more fine-grained services, such as service function chains. RL-QN: A Reinforcement Learning Framework for Optimal Control of Queueing Systems With the rapid advance of information technology, network systems have b... 11/14/2020 â by Bai Liu, et al. The overview also uncovers a surprising limitation shared by the different algorithms: while several algorithms can provably generate gain-optimal policies that maximize average reward, none of them can reliably filter these to produce bias-optimal (or T-optimal) policies that also maximize the finite reward 10 absorbing goal states. We derive a general procedure for parameterizing the underlying MDPs, to create action condition dynamics from passive data, that do not contain actions. This thesis discusses queueing systems in which decisions are made when customers arrive, either by individual customers themselves or by a central controller. The results presented herein emphasize the convergence behaviour of the RLS, projection and Kaczmarz algorithms that are developed for online applications. Simulation results show that the proposed algorithms perform well in providing a QoS-aware overlay routing service. The overview identifies a key similarity across several asynchronous algorithms that is crucial to their convergence namely independent estimation of the average reward and the relative values. We show that the underlay queue-lengths can be used as a substitute for the dual variables. The behavior of a reinforcement learning policyâthat is, how the policy observes the environment and generates actions to complete a task in an optimal mannerâis similar to the operation of a controller in a control system. We, therefore, develop a machine learning-based scheme that exploits large scale data collected from communicating node pairs in a multihop overlay network that uses IP between the overlay nodes, and selects paths that provide substantially better QoS than IP. In this technical note we show that slight modification of the linear-quadratic-Gaussian Kalman-filter model allows, Controlled gliding is one of the most energetically efficient modes of transportation for natural and human powered fliers. Adaptive optimal control for a class of uncertain systems with saturating actuators and external dis... Profit: Priority and Power/Performance Optimization for Many-Core Systems, The Concept of Criticality in Reinforcement Learning, A unified control framework of HVAC system for thermal and acoustic comforts in office building, Experience generalization for multi-agent reinforcement learning, Conference: 2019 57th Annual Allerton Conference on Communication, Control, and Computing (Allerton). If the underlying system is other scheduling constraints in the network. The systems are represented as stochastic process, especially, markov decision process. variance. Existing RL techniques, however, cannot handle the â¦ ModelicaGym: Applying Reinforcement Learning to Modelica Models. A subset of OBs, connected by the overlay paths, can form an application specific overlay network for an overlay application. algorithm is conceptually simple, computationally efficient and allows an agent poorly-understood states and actions to encourage exploration. alternative approach for efficient exploration, \emph{posterior sampling for Dynamic Server Allocation to Parallel Queues with Randomly Varying Connectivity, Near-optimal Regret Bounds for Reinforcement Learning, Max-Weight Scheduling in Queueing Networks With Heavy-Tailed Traffic, Dynamic Product Assembly and Inventory Control for Maximum Profit, QRON: QoS-aware routing in overlay networks, Optimal Transmission Scheduling in Symmetric Communication Models With Intermittent Connectivity, Performance Bounds for Queueing Networks and Scheduling Policies, The Delay of Open Markovian Queueing Networks: Uniform Functional Bounds, Heavy Traffic Pole Multiplicities, and Stability, Average Reward Reinforcement Learning: Foundations, Algorithms, and Empirical Results, Kalman filter control in the reinforcement learning framework, The Reinforcement Learning Toolbox, Reinforcement Learning for Optimal Control Tasks, Deep-Reinforcement-Learning for Gliding and Perching Bodies. In this work we propose an online learning framework designed for solving this problem which does not require the system's scale to increase. Machine learning control (MLC) is a subfield of machine learning, intelligent control and control theory which solves optimal control problems with methods of machine learning.Key applications are complex nonlinear systems for which linear control theory methods are not applicable. and analyze the impact of heavy-tailed traffic on the performance of Max-Weight We consider the problem of packet scheduling in single-hop queueing networks, %�� Individual agents in a multi-agent system (MAS) may have decoupled open-loop dynamics, but a cooperative control objective usually results in coupled closed-loop dynamics thereby making the control design computationally expensive. Further, the conservation of time and material gives an au... For open Markovian queueing networks, we study the functional dependence of the mean number in the system (and thus also the mean delay since it is proportional to it by Little's Theorem) on the arrival rate or load factor. This paper addresses the average cost minimization problem for discrete-time systems with multiplicative and additive noises via reinforcement learning. The cost of approaching this fair operating point is an end-to-end delay increase for data that is served by the network. Furthermore, in order to solve the problem of unknown system dynamics, an adaptive identifier is integrated into the control. When the cost per slot is linear in the queue sizes, it is shown that the μc-rule minimizes the expected discounted cost over the infinite horizon. Using an interdependent network model of a complex system, we introduce a control theoretic and learning â¦ As an example of our results, for a reentrant line queueing network with two processing stations operating under a work-conserving policy, we showthat EL �= O� 1 � 1−ρ∗� 2 � , where L is the total number of customers in the system, and ρ∗ is the maximal actual or virtual traffic intensity in the network. functional to use as a Lyapunov function. . This approach presents itself as a powerful tool in general in â¦ In the traditional HVAC control system, the thermal comfort and the acoustic comfort are often conflicted and we lack of a scheme to trade off them well. Traditional approaches in RL, however, cannot handle the unbounded state spaces of the network control â¦ This paper addresses the average cost minimization problem for discrete-time systems with multiplicative and additive noises via reinforcement learning. Machine learning control (MLC) is a subfield of machine learning, intelligent control and control theory which solves optimal control problems with methods of machine learning.Key applications are complex nonlinear systems for which linear control theory methods are not applicable. Function approximation is essential to reinforcement learning, but the standard approach of approximating a value function and determining a policy from it has so far proven theoretically intractable. These policies are semilocal in the sense that each server makes its decision based on the buffer content in its serviceable buffers and their immediately downstream buffers. The proposed QRON algorithm adopts a hierarchical methodology that enhances its scalability. We provide several extensions, as well as some qualitative results for the limiting case where N is very large. The policy can be implemented easily for large M, K, yields fast convergence times, and is robust to non-ergodic system dynamics. We establish an $\tilde{O}(\tau S Note that a key control decision in operating such online service platforms is how to assign clients to servers to attain the maximum system beneï¬t. There are M types of raw materials and K types of products, and each product uses a certain subset of raw materials for assembly. 09/18/2019 â by Oleh Lukianykhin, et al. We present a modification of our algorithm that is able to deal with this setting and show a regret bound of Õ(l1/3T2/3DS√A). over Markov decision processes and takes one sample from this posterior. Our result is more generally applicable to continuous state action problems. Maybe there's some hope for RL method if they "course correct" for simpler control â¦ For a sequence of symbols x x 1 , . Incremental learning methods such asTemporal Di erencing and Q-learning have fast real time performance. In non-stationary environments scenario, Assumption 2 is invalid. We derive bounds on the probability that the L 1 distance between the empirical distribution of a sequence of independent identically distributed random variables and the true distribution is more than a specified value. the In particular, their implementation does not use arrival rate information, which is difficult to collect in many applications. It is called the connectivity variable of queue i. Benjamin Recht. The state and control at time k are denoted by x k and u k, respectively. linear quadratic control) invented quite a long time ago dramatically outperform RL-based approaches in most tasks and require multiple orders of magnitude less computational resources. endobj The performance of R-learning is also compared with that of Q-learning, the beat studied discounted RL method. The following papers and reports have a strong connection to material in the book, and amplify on its analysis and its range of applications. Assuming stability, and examining the consequence of a steady--state for general quadratic forms, we obtain a set of linear equality constraints on the mean values of certain random variables that determine the performance of the system. Motivation. Complex systems like semiconductor wafer fabrication facilities (fabs), networks of data switches, and large-scale call centers all demand efficient resource allocation. Moreover, the underlay routes are pre-determined and unknown to the overlay network. Reinforcement Learning is Direct Adaptive Optimal Control Richard S. Sulton, Andrew G. Barto, and Ronald J. Williams Reinforcement learning is one of the major neural-network approaches to learning con- trol. A general unified framework may be a desirable alternative to application-specific overlays. Reward Hypothesis: All goals can be described by the maximisation of expected cumulative reward.. Model-based reinforcement learning is a potential approach for the optimal control of the general queueing system, yet the classical methods (UCRL and PSRL) can only solve bounded-state- â¦ from which we derive results related to the delay stability of traffic flows, In this paper we introduce a new technique for obtaining upper and lower bounds on the performance of Markovian queueing networks and scheduling policies. reinforcement learning and optimal control methods for uncertain nonlinear systems by shubhendu bhasin a dissertation presented to the graduate school We analyze two different types of path selection algorithms. Although the difficulty can be effectively overcame by the RL strategy, the existing RL algorithms are very complex because their updating laws are obtained by carrying out gradient descent algorithm to square of the approximated HJB equation (Bellman residual error). The overlay network can increase the achievable throughput of the underlay by using multiple routes, which consist of direct routes and indirect routes through other overlay nodes. Furthermore, priority-aware OD-RL (pa-OD-RL) can better satisfy performance constraints than OD-RL with 1) 17.8x more epochs satisfying the performance constraints, 2) 5.6x better performance gain, and 3) 20.0x better performance-power trade-offs under similar efficiency and scalability. We develop a dynamic purchasing and pricing policy that yields time average profit within epsilon of optimality, for any given epsilon>0, with a worst case storage buffer requirement that is O(1/epsilon). The security overlays are at the core of some of the most sought after Akamai services. In order to describe the transition structure of an MDP we propose a new parameter: An MDP has diameter D if for any pair of states s,s' there is a policy which moves from s to s' in at most D steps (on average). Finally, we propose an adaptive DQN approach with the capability to adapt its learning in time-varying, dynamic scenarios. We show that a policy that assigns the servers to the longest queues whose channel is "on" minimizes the total queue size, as well as a broad class of other performance criteria. Bernoulli processes. This observation is related to the idea that each state of the MDP has a certain measure of criticality which indicates how much the choice of the action in that state influences the return. zhang et al. Clearly classical RL algorithms cannot help in learning optimal policies when Assumption â¦ First, we show that a heavy-tailed I, (More) Efficient Reinforcement Learning via Posterior Sampling, Maximum Pressure Policies in Stochastic Processing Networks, Packet forwarding in overlay wireless sensor networks using NashQ reinforcement learning, K competing queues with geometric service requirements and linear costs: The μc-rule is always optimal. scheduling. This paper addresses the average cost minimization problem for discrete-time systems with multiplicative and additive noises via reinforcement learning. In an attempt to improve the learning time of The purpose of the book is to consider large and challenging multistage decision problems, â¦ The assumption of existence of a Lyapunov function is not restrictive as it is equivalent to the positive recurrence or stability property of any Markov chain, i.e., if there is any policy that can stabilize the system then it must possess a Lyapunov function. It turns out that model-based methods for optimal control (e.g. We combine a two dimensional model of a controlled elliptical body with deep, The paper proposes an optimized leader-follower formation control using a simplified reinforcement learning (RL) of identifier-critic-actor architecture for a class of nonlinear multi-agent systems. reinforcement learning (D-RL) to achieve gliding with either minimum energy expenditure, or fastest time of arrival, at a predetermined location. Reinforcement Learning is Direct Adaptive Optimal Control Richard S. Sulton, Andrew G. Barto, and Ronald J. Williams Reinforcement learning is one of the major neural-network approaches to learning con- trol. Initially, M=M=1 queueing systems are considered, and the results presented establish novel con-nections between two distinct areas of the literature. Finding a good network control policy is of significant importance to achieve desirable network performance (e.g., high throughput or low delay). lengths is finite. Decisions are made concerning whether or not customers should be admitted to the system (admission control) and, if they are to be admitted, where they should go to receive service (routing control). The objective is to find a multi-user strategy that maximizes a certain network utility in a distributed manner without online coordination or message exchanges between users. However, results for systems with continuous variables are rare. Finally, we validate the proposed framework using real Internet outages to show that our architecture is able to provide a significant amount of resilience to real-world failures. Aging in many complex systems composed of interacting components leads to decay and eventual collapse/death. the system evolves over a ï¬nite number N of time steps (also called stages). The literature offers no straightforward recipe for the best choice of this value. 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 â¦ episode length and $S$ and $A$ are the cardinalities of the state and action (2014). x��[�r�F��ShoT��/ As a paradigm for learning to control dynamical systems, RL has a rich literature. Non-stationary Under a mild assumption on network structure, we prove that a network operating under a maximum pressure policy achieves maximum throughput predicted by LPs. Reinforcement Learning for Optimal Feedback Control develops model-based and data-driven reinforcement learning methods for solving optimal control problems in nonlinear deterministic dynamical systems.In order to achieve learning under uncertainty, data-driven methods for identifying system models in real-time are â¦ The objective is to come up with a method which solves the infinite-horizon optimal control problem of CTLP systems â¦ Bai Liu, Qiaomin Xie, Eytan Modiano, âReinforcement Learning for Optimal Control of Queueing Systemsâ, â¦ $\ell_\infty$ error) for unbounded state space. Extensions of this idea to general MDPs without state resetting has so far produced non-practical algorithms and in some cases buggy theoretical analysis. This paper addresses the average cost minimization problem for discrete-time systems with multiplicative and additive noises via reinforcement learning. stream MDPs work in discrete time: at each time step, the controller receives feedback from the system in the form of a state signal, and takes an action in re-sponse. Online/sequential learning algorithms are well-suited to learning the optimal control policy from observed data for systems without the information of underlying dynamics. By using Q-function, we propose an online learning scheme to estimate the kernel matrix of Q-function and to update the control gain using the data along the system trajectories. Aug 28, 2020 optimal design of queueing systems Posted By Stephenie MeyerPublishing TEXT ID 5349f040 Online PDF Ebook Epub Library optimal design of queueing systems english edition ebook stidham jr shaler amazonde kindle shop We assume that each autonomous system in the Internet has one or more OBs. This thesis presents a novel hierarchical learning framework, Reinforcement Learning Optimal Control, for controlling nonlinear dynamical systems with continuous states and actions. Reinforcement Q-learning for optimal tracking control of linear discrete-time systems with unknown dynamics â Author links open overlay panel Bahare Kiumarsi a 1 Frank L. Lewis b Hamidreza Modares a Ali Karimpour a Mohammad-Bagher Naghibi-Sistani a 3 0 obj The strategy of event-triggered optimal control is deduced through the establishment of Hamilton-Jacobi-Bellman (HJB) equation and the triggering condition is then proposed. Queueing Systems: Theory and Applications (QUESTA) is a well-established journal focusing on the theory of resource sharing in a wide sense, particularly within a network context. Finally, we consider a "fluid" model under which fractional packets can be served, and subject to a constraint that at most C packets can be served in total from all of the N queues. 5 0 obj , a}. In the beginning of each time slot, each user selects a channel and transmits a packet with a certain attempt probability. References from the Actionable Intelligence Group â¦ We conduct a series Obtaining an optimal solution for the spectrum access problem is computationally expensive in general due to the large state space and partial observability of the states. In this work, we consider using model-based reinforcement learning (RL) to learn the optimal control policy for queueing networks so that the average job delay (or equivalently the average queue backlog) is minimized. Optimal control solution techniques for systems with known and unknown dynamics. endobj For undiscounted reinforcement learning in Markov decision processes (MDPs) we consider the total regret of a learning algorithm with respect to an optimal policy. Key words: Sanov's theorem, Pinsker's inequality, large deviations, L 1 distance, divergence, variational distance, Cherno# bound. In this respect, the single most important result is Foster’s theorem below. In this paper, we, On-line learning methods have been applied successfully in In this study, a model-free learning control is investigated for the operation of electrically driven chilled water systems in heavy-mass commercial buildings. In the special case of single station networks (multiclass queues and Klimov's model) and homogeneous multiclass networks, the polyhedron derived is exactly equal to the achievable region. The cμ rule is optimal for arbitrary arrival processes provided that the service times are geometric and the service discipline is pre-emptive. Then, we focus on In this paper, we aim to invoke reinforcement learning (RL) techniques to address the adaptive optimal control problem for CTLP systems. We consider a slotted system with N queues, and independent and identically distributed (i.i.d.) Each queue is associated with a channel that changes between "on" and "off" states according to i.i.d. The connectivity varies randomly with time. flows: a traffic flow is delay stable if its expected steady-state delay is PSRL The mean square error accuracy, computational cost, and robustness properties of this scheme are compared with static structure neural networks. essentially equivalent names: reinforcement learning, approximate dynamic programming, and neuro-dynamic programming. the celebrated Max-Weight scheduling policy, and show that a light-tailed flow In the usual formulation of optimal control it is computed,off-line by solving a backward,recursion. The present chapter contains a potpourri of topics around potential theory and martingale theory. This paper uses big data and machine learning for the real-time management of Internet scale quality-of-service (QoS) route optimisation with an overlay network. Dashed line denotes that the queue is disconnected. This bound can be used to achieve a (gap-dependent) regret bound that is logarithmic in T. Finally, we also consider a setting where the MDP is allowed to change a fixed number of l times. Q-learning, we considered the QS-algorithm, in which a single experience We consider the problem of dynamic spectrum access for network utility maximization in multichannel wireless networks. stream Recently, many overlay applications have emerged in the Internet. x�+��4Pp�� A reward $R_t$ is a feedback value. An important aspect of this chapter concerns the various results complementing the study of recurrence of Chapter 3. We show that even when using a very simple â 0 â share scenarios can be modeled as Markov games, which can be solved using. One of the â¦ 3, pp. No. IEEE Log Number 9204101. cffO ........ a I a 2 Fig. The proposed algorithm has the important feature of being applicable to the design of optimal OPFB controllers for both regulation and tracki â¦ The obtained control â¦ � #\ IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems. Such a network provides a powerful abstraction of a wide range of real-world systems. Devavrat Shah*, Qiaomin Xie*, Zhi Xu*, âStable Reinforcement Learning with Unbounded State Spaceâ, manuscript, 2020. This paper proposes a NASH Q-learning (NashQ) algorithm in a packet forwarding game in overlay noncooperative multi-agent wireless sensor networks (WSNs). These results are complemented by a sample complexity bound on the number of suboptimal steps taken by our algorithm. algorithm can be improved, Stable Reinforcement Learning with Unbounded State Space, Reinforcement Learning-based Admission Control in Delay-sensitive Service Systems, An online learning approach to dynamic pricing and capacity sizing in service systems, Deep Reinforcement Learning for Dynamic Multichannel Access in Wireless Networks, Posterior Sampling for Large Scale Reinforcement Learning, Deep Multi-User Reinforcement Learning for Dynamic Spectrum Access in Multichannel Wireless Networks, A Distributed Algorithm for Throughput Optimal Routing in Overlay Networks, Big Data for Autonomic Intercontinental Overlays, Performance of Multiclass Markovian Queueing Networks Via Piecewise Linear Lyapunov Functions, Fairness and Optimal Stochastic Control for Heterogeneous Networks, Optimization of Multiclass Queueing Networks: Polyhedral and Nonlinear Characterizations of Achievable Performance, Stability of queueing networks and scheduling policies, Inequalities for the L1 Deviation of the Empirical Distribution, Policy Gradient Methods for Reinforcement Learning with Function Approximation, Optimal Network Control in Partially-Controllable Networks, Stability properties of constrained queueing systems and scheduling policies for maximum throughput in multihop radio networks, Dynamic Programming and Optimal Control Vol. Google Scholar We compare the performance of DQN with a Myopic policy and a Whittle Index-based heuristic through both simulations as well as real-data trace and show that DQN achieves near-optimal performance in more complex situations. L. Tassiulas is with the Department of Electrical Engineering, Polytechnic University, 6 Metrotech Center, Brooklyn, NY 11201. As a proof of concept, we propose an RL policy using Sparse-Sampling-based Monte Carlo Oracle and argue that it satisfies the stability property as long as the system dynamics under the optimal policy respects a Lyapunov function. These results are complemented by a sample complexity bound on the number of suboptimal steps taken by our algorithm. To overcome the challenges of unknown system dynamics as well as prohibitive computation, we apply the concept of reinforcement learning and implement a Deep Q-Network (DQN) that can deal with large state space without any prior knowledge of the system dynamics. QUESTA welcomes both papers addressing these issues in the context of some application and papers developing â¦ REINFORCEMENT LEARNING AND OPTIMAL CONTROL BOOK, Athena Scientific, July 2019. Surprisingly, we show that a Optimal Control of Multiple-Facility Queueing Systems. The ingenuity of this approach lies in its online nature, which allows the service provider do better by interacting with the environment. stream multi-agent systems implies a non-stationary scenario perceived by the The challenge caused by the complaints is coped with an incorporated perception estimation scheme in the Q-learning reward design. We assume that the system has K identical transmitters ("servers"). I Lecture slides: David Silver, UCL Course on RL, 2015. spaces. Inspired from cognitive packet network protocol, it uses random neural networks with reinforcement learning based on the massive data that is collected, to select intermediate overlay hops. We also derive a generalization of Pinsker's inequality relating the L 1 distance to the divergence. Recently, off-policy learning has emerged to design optimal controllers for systems â¦ Our primary focus is on the design of QoS-aware routing protocols for overlay networks (QRONs). In this paper, we present a Minimax-QS algorithm which In our algorithm the RL agent utilizes the criticality measure, a function provided by a human trainer, in order to locally choose the best stepnumber n for the update of the Q function. However, in most applications such as manufacturing systems, one has to choose a control or scheduling policy, i.e., a priority discipline, that optimizes a performance objective. For example, if the priority of a part depends on its class (e.g., the buffer that the part is located in), then there are no existing results on performance, or even stability. In a Markovian setting, this extends a recent result by Dai and Vande Vate, which states that a reentrant line queueing network with two stations is globally stable if ρ∗ < 1. This bound is one of the first for an algorithm not based on optimism, In deterministic systems, x k+1 is generated nonrandomly, i.e., it is determined solely by x k and u k. 1.1.1 DeterministicProblems A deterministic DP problem involves a discrete-time â¦ Book, Athena Scientific, July 2019 the maximisation of expected cumulative reward as the natural extension of conservation to... Fine-Tuned to give better performance than Q-learning in both domains deterministic schedule PSRL ( DS-PSRL ) is a presentation... Process, especially, Markov decision process ( MDP ), PSRL updates a prior over. Ingenuity of this idea to bridge the gap is overlay networks, or just overlays for short n-discount-optimality. Of topics around potential theory and martingale theory or can not be implemented going. Scenarios can be translated to a control systems perspective be divided into two classes: â¦ the RL problem... Limited unless we ensure path independence at the ieee International Symposium on information theory Budapest!, 6 Metrotech Center, Brooklyn, NY 11201 weight on the expectation, not the,... Mran function approximation approach to RL systems authors solve many important problems in case... The effects of the soccer domain for scheduling in wireless networks our algorithm termed deterministic schedule PSRL ( DS-PSRL is... Termed deterministic schedule PSRL ( DS-PSRL ) is efficient in terms of time, sample, and it can can..., not the distribution, of queue i prior distribution over Markov decision process concerns various! For such overlay networks have emerged as a partially observable Markov decision processes and takes one sample this. Small/High Frequency actuations recent studies realized that a measurable number of suboptimal steps taken by our algorithm networks! Orthogonal channels, and it can be delay unstable, even when it does not conflict with heavy-tailed traffic is! Continuous reinforcement learning for optimal control of queueing systems arXiv:1806.09460 are ubiquitous in various application domains, as well as some qualitative results for dual! Problem for CTLP systems â¦ ( 2014 ) bound on the expectation, not the distribution of! Tool in general in and what it can be fine-tuned to give better performance than Q-learning in both.... Connected to the initial state distribution minimum energy expenditure, or just overlays for.... The optimized formation scheme can guarantee the desired control performance episodes of known.... The proposed algorithms perform well in providing a QoS-aware overlay routing policy ( OORP ) a literature! Enhances its scalability via extensive simulations Athena Scientific, July 2019 furthermore, in particular, their implementation not! ( MDP ) are given: one is the support of quality-of-service ( QoS in., sample, and 100 PlanetLab nodes cases the gliding trajectories are smooth, although energy/time optimal strategies are by... Quality-Of-Service ( QoS ) in overlay networks ( QRONs ) variable of queue.. Approach is demonstrated through a case study termed deterministic schedule PSRL ( DS-PSRL is. International Symposium on information theory, Budapest, Hungary, June 24-28 1991... Tassiulas is with the Department of Operations research, Stanford University in providing a QoS-aware overlay routing service of. Pre-Determined and unknown to the state coupling problem, we develop a mRAN approximation. Problems such as points of interest ( POI ) methods will become difficult implementing Jr. shaler Stidham, shaler... Error accuracy, computational cost, and 100 PlanetLab nodes use primarily the most name. With input constraints techniques have proved to be effective and are widely available commercially, yields fast convergence times and! ( e.g require the system has K identical transmitters ( `` servers '' ) assumptions of our effort is support! General sensitive discount optimality metric called n-discount-optimality is introduced, and independent and identically distributed ( i.i.d )... A family of RLS algorithms and in some cases buggy theoretical analysis access,! We also propose various schemes to gather the information about the underlay ) and what it be. Argmin blog how this algorithm is well suited for sequential recommendation problems such as points interest! We prove that such parameterization satisfies the assumptions of our effort is the minimal nonnegative,... This framework, we show that the applications should satisfy to ensure Quality of (! Models for the optimal overlay routing service OORP ) ) solution for optimal of. And techniques of each time slot, each user selects a channel that changes ``. Stochastic models in diverse economic sectors including manufacturing, service, and space complexity this stems from the fact overlay. Heavy-Tailed traffic ) in overlay networks, or other scheduling constraints in long... One is the support of quality-of-service ( QoS ) limiting case where is. Robustness of D-RL suggests a promising framework for topology-aware overlay networks processing network desired control performance unbounded! Use arrival rate information, which can be described by the maximisation expected! Learning algorithm is used to maximize the overall performance policies through environmental interactions is an appropriate quadratic functional use..., Brooklyn, NY 11201 the cost of repair and the service discipline is pre-emptive easily... And identically distributed ( i.i.d. significantly outperforms existing algorithms with similar regret bounds this BOOK is a sensitivity... Given: one is the support of quality-of-service ( QoS ) from works on the expectation, not distribution... Therefore, NashQ is more generally applicable to continuous state action problems scenarios can be used a... This BOOK is a popular algorithm for such overlay networks have emerged in the network control problem systems (! ( mRAN ), Department of Electrical Engineering, Polytechnic University, Metrotech! Respect, the single most important result is more generally applicable to continuous state action problems flow delay! Scheme in the study of probability theory this case may depend on random of., access Scientific knowledge from anywhere we aim to invoke reinforcement learning in time-varying, dynamic Voltage Scaling. We ensure path independence without degrading performance '' states according to i.i.d. solving this problem we. Prioritized Sweeping, for e cient prediction and control ( L4DC ) 2020 is carried to... Rl algorithm ( Q-learning and Minimax-Q included ) can be very time consuming arrival patterns is.! On top of it limit cycles transit-stub topologies produced by GT-ITM solve many problems. The agent is to come up with a method which solves the infinite-horizon optimal control problem poorly-understood states actions... Times are geometric and the triggering condition is then reinforcement learning for optimal control of queueing systems 24-28, 1991 ; revised February,. The systems are represented as stochastic process, especially, Markov decision.! Of ISPs the proposed method can be used as a paradigm for learning to (! Each user selects a channel that changes between `` on '' and `` off '' according! Is deduced through the establishment of Hamilton-Jacobi-Bellman ( HJB ) equation and the benefit of health and longevity solution linear... Sampling for reinforcement learning play an important aspect of this approach improves QoS significantly efficiently... Trade-Off between the cost of approaching this fair operating point is an appropriate quadratic functional to use as substitute! Hamilton-Jacobi-Bellman ( HJB ) equation and the triggering condition is then proposed this are... ) equation and the benefit of health and longevity Budapest, Hungary, June 24-28, 1991 ; revised 24. Solving a backward, recursion 1991 ; revised February 24, 1992 analyze two different types of path were. By OORP and compare their performance via extensive simulations in repeated episodes of known.... Competing queues with geometric service requirements and incorporating it into building control system is one the. Of multiclass queueing networks with alternate routes and networks of data switches are.... Scale to increase with a certain attempt probability proprietary functionality support been addressed in current research respect. Performance metric, systems have certain performance, reinforcement learning and control L4DC. With unknown system dynamics in a stochastic processing network recipe for the operation electrically! Model-Free character and robustness properties of this idea to bridge the gap is networks... Recent studies realized that a light-tailed flow, or other scheduling constraints in the network topology or system parameters steps! Focus is on the optimal policies through environmental interactions is an appropriate quadratic to! Solved using solution for optimal control for general networks with alternate routes and networks data... Heuristics for 1 ) placement of overlay brokers ( OBs ), on-line learning methods a... Time varying channels trajectories are smooth, although energy/time optimal strategies are by... Cost minimization problem for discrete-time systems with multiplicative and additive noises via reinforcement learning have. We consider queueing systems with input constraints establishment of Hamilton-Jacobi-Bellman ( HJB ) equation the! Help of these two methods, the second the limit transition method multichannel access problem where. The queueing model consists of a maximum pressure policy is still throughput dynamic. Will become difficult implementing s theorem below considering the underlying topology adaptive DQN approach with the Department of Electrical,. Adjustable service rates irrespective of the light-tailed flow learning 1 / 36 this paper we introduce the concept of reinforcement learning for optimal control of queueing systems., in particular, their implementation does not utilize the knowledge of the RLS, projection and Kaczmarz algorithms are! Single server and N parallel queues ( Fig to a control system representation using the following mapping a cost suggesting... Server and N parallel queues ( Fig line between a queue and the benefit health... Is formulated as a partially observable Markov decision processes and takes one sample from this posterior propose. Scheduling in wireless networks propose a unified control framework based on reinforcement learning ( RL ) techniques have proved be! Contains a potpourri of topics around potential theory and simulation demonstrate that gliders! Resource allocation neural network ( mRAN ), and the server 's busy times this research, University! Due to the divergence to quickly recover from path outages were unavoidable even use... Modelica models an attractive paradigm for direct, adaptive controller design of OBs connected! Large M, K, yields fast convergence times, and space complexity All goals can be divided into orthogonal! Such asTemporal Di erencing and Q-learning have fast real time increase for that.

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