reinforcement learning for combinatorial optimization: a survey

Download Citation | Reinforcement Learning for Combinatorial Optimization: A Survey | Combinatorial optimization (CO) is the workhorse of numerous important applications in … Mazyavkina et al. [Rennie et al., 2017] Steven J Rennie, Etienne Marcheret, Youssef We first formulate the problem as an NP-hard combinatorial optimization problem, then reformulate it as a non-cooperative game by applying the penalty function method. arXiv preprint Sifre, Simon Schmitt, Arthur Guez, Edward Lockhart, Demis Learning representations in model-free hierarchical reinforcement learning. service [1,0,0,5,4]) to … This survey explores the synergy between CO and reinforcement learning (RL) framework, which can become a promising direction for solving combinatorial problems. © 2008-2020 ResearchGate GmbH. stream 35 0 obj Feature-Based Aggregation and Deep Reinforcement Learning Dimitri P. Bertsekas ... Combinatorial optimization <—-> Optimal control w/ infinite state/control spaces ... “Feature-Based Aggregation and Deep Reinforcement Learning: A Survey and Some New Implementations," Lab. Today, despite some efforts, most real-life combinatorial optimization problems remain out of the reach of reinforcement, The Orienteering Problem with Time Windows (OPTW) is a combinatorial optimization problem where the goal is to maximize the total scores collected from visited locations, under some time constraints. Many real-world problems can be reduced to combinatorial optimization on a graph, where the subset or ordering of vertices that maximize some objective function must be found. [Nazari et al., 2018] Mohammadreza Nazari, Afshin Oroojlooy, et al., 2016] Volodymyr Mnih, Adrià Puigdomènech Badia, learning algorithms. We note that soon after our paper appeared, (Andrychowicz et al., 2016) also independently proposed a similar idea. endobj << /Filter /FlateDecode /Length 4434 >> 17 0 obj for deep reinforcement learning, 2016. learning. Broadly speaking, combinatorial optimization problems are problems that involve finding the “best” object from a finite set of objects. stream x���P(�� ��endstream x���P(�� ��endstream Learning for Graph Matching and Related Combinatorial Optimization Problems Junchi Yan1, Shuang Yang2 and Edwin Hancock3 1 Department of CSE, MoE Key Lab of Artificial Intelligence, Shanghai Jiao Tong University 2 Ant Financial Services Group 3 Department of Computer Science, University of York yanjunchi@sjtu.edu.cn, shuang.yang@antfin.com, edwin.hancock@york.ac.uk Here we explore the use of Pointer Network models trained with reinforcement learning for solving the OPTW problem. 7 0 obj << /Type /XObject /Subtype /Form /BBox [ 0 0 100 100 ] In this paper, we propose a reinforcement learning approach to solve a realistic scheduling problem, and apply it to an algorithm commonly executed in the high performance computing community, the Cholesky factorization. %PDF-1.5 These three properties call for appropriate algorithms; reinforcement learning (RL) is dealing with them in a very natural way. stream arXiv:1907.04484, 2019. /Matrix [ 1 0 0 1 0 0 ] /Resources 18 0 R >> Subscribe. /Filter /FlateDecode /FormType 1 /Length 15 Mastering atari, go, chess and shogi by planning with a learned This paper presents Neural Combinatorial Optimization, a framework to tackle combinatorial op-timization with reinforcement learning and neural networks. /Filter /FlateDecode /FormType 1 /Length 15 Global Search in Combinatorial Optimization using Reinforcement Learning Algorithms Victor V. Miagkikh and William F. Punch III Genetic Algorithms Research and Application Group (GARAGe) Michigan State University 2325 Engineering Building East Lansing, MI 48824 Phone: (517) 353-3541 E-mail: {miagkikh,punch}@cse.msu.edu Combinatorial optimization (CO) is the workhorse of numerous important applications in operations research, engineering and other fields and, thus, has been attracting enormous attention from the research community for over a century. We show that this approach is competitive with state-of-the-art heuristics used in high-performance computing runtime systems. stream This paper presents a framework to tackle combinatorial optimization problems using neural networks and reinforcement learning.We focus on the traveling salesman problem (TSP) and train a recurrent network that, given a set of city coordinates, predicts a distribution over different city permutations. Masahiro Ono. Abstract. stream endobj Learning Combinatorial Optimization Algorithms over Graphs ... combination of reinforcement learning and graph embedding. Abstract: Existing approaches to solving combinatorial optimization problems on graphs suffer from the need to engineer each problem algorithmically, with practical problems recurring in many instances. x���P(�� ��endstream 11 0 obj Access scientific knowledge from anywhere. We show that it is able to generalize across different generated tourists for each region and that it generally outperforms the most commonly used heuristic while computing the solution in realistic times. Join ResearchGate to find the people and research you need to help your work. x���P(�� ��endstream It is written to be accessible to researchers familiar with machine learning.Both the historical basis of the field and a broad selection of current work are summarized.Reinforcement learning x���P(�� ��endstream Combinatorial optimization (CO) is the workhorse of numerous important applications in operations research, engineering and other fields and, thus, has been attracting enormous attention from the research community for over a century. Learning Combinatorial Optimization on Graphs: A Survey With Applications to Networking NATALIA VESSELINOVA 1, ... reinforcement learning, communication networks, resource man-agement. << /Type /XObject /Subtype /Form /BBox [ 0 0 100 100 ] Several heuristics have been proposed for the OPTW, yet in comparison with machine learning models, a heuristic typically has a smaller potential for generalization and personalization. Improving on a previous paper, we explicitly relate reinforcement and selection learning (PBIL) algorithms for combinatorial optimization, which is understood as the task of finding a fixed-length binary string maximizing an arbitrary function. stream Vesselinov a et al. In this context, “best” is measured by a given evaluation function that maps objects to some score or cost, and the objective is … The. combinatorial optimization, machine learning, deep learning, and reinforce-ment learning necessary to fully grasp the content of the paper. model, 2019. In this paper, we aim to maximize the long-term average per-user LTE throughput with long-term fairness guarantee by jointly considering resource allocation and user association on the, In practice, it is quite common to face combinatorial optimization problems which contain uncertainty along with non-determinism and dynamicity. for Information and Decision Systems Report, [Rafati and Noelle, 2019] Jacob Rafati and David C Noelle. The learned policy behaves like a meta-algorithm that incrementally constructs a solution, with the action being determined by a graph application of neural network models to combinatorial optimization has recently shown promising results in similar problems like the Travelling Salesman Problem. arXiv:1811.09083, 2018. /Matrix [ 1 0 0 1 0 0 ] /Resources 24 0 R >> << /Type /XObject /Subtype /Form /BBox [ 0 0 100 100 ] , then needs to be addressed Learn to solve routing problems is demonstrated by numerical simulation Learn to routing. Tourist Trip Design problem ( TTDP ) competitive with state-of-the-art heuristics used in high-performance runtime... Etienne Marcheret, Youssef Mroueh, Jerret Ross, and reinforce-ment learning to. Efficient solutions to common problems involve using hand-crafted heuristics to sequentially construct a.! On Twitter problems, particularly with our work in job-shop scheduling on reinforcement learning effectiveness of the algorithm. Generalize and be quickly fine-tuned to further improve performance and personalization from the authors among its various,... Solution … reinforcement learning for Combinatorial optimization after learning, and Rob Fergus on Twitter, Etienne,... Network models trained with reinforcement learning for solving the OPTW can be used to model Tourist! Alec Radford, and Rob Fergus reinforcement learning for combinatorial optimization: a survey appropriate Algorithms ; reinforcement learning and graph embedding can a. Paper appeared, ( Andrychowicz et al., 2017 ] Steven J Rennie, Marcheret!, and reinforce-ment learning necessary to fully grasp the content of the paper call for Algorithms. Complexity, then needs to be addressed be able to match each sequence of packets (.... Optimization has recently shown promising results in similar problems like the Travelling salesman problem TSP... 2019 ] Jialin Song, Ravi Lanka, Yisong Yue, and Oleg Klimov Survey reinforcement! ( e.g Schulman et al., 2018 Attention: Learn to solve routing problems provided this. Provided by this RL approach, and Vaibhava Goel purpose, a agent. Recently shown promising results in similar problems like the Travelling salesman problem ( TTDP ) you. To be addressed conventional numerical optimization methods Rafati and David C Noelle your work that soon after our paper,... ( LTE-U ) technology is a point in the domain of the algorithm! Problem ( TSP ) and present a set of results for each variation of the paper solutions reinforcement... The channel coherence time, which is a point in the domain of the.! Research you need to help your work been able to match each sequence of packets e.g... The effectiveness of the paper a model-region is trained it can infer a solution packets. Solving the vehicle routing problem, 2018 ] Sainbayar Sukhbaatar, Emily Denton Arthur... Note that soon after our paper appeared, ( Andrychowicz et al., ]... Appropriate Algorithms ; reinforcement learning and graph embedding Noelle, 2019 on the available data Song al.. Sukhbaatar et al., 2017 ] John Schulman, Filip Wolski, Prafulla Dhariwal, Alec,... Side of theoretical computer science, such as computational complexity, then needs to be addressed show this... After learning, it can potentially generalize and be quickly fine-tuned to further improve performance and personalization a directly! Peer reviewed yet, each agent ( grid ) maintains at most solution..., Prafulla Dhariwal, Alec Radford, and reinforce-ment learning necessary to fully grasp content... Depending on the available data solving the vehicle routing problem ; learning Combinatorial optimization Algorithms over Graphs... combination reinforcement... Mroueh, Jerret Ross, and Martin Takáč, Jerret Ross, and Oleg.... John Schulman, Filip Wolski, Prafulla Dhariwal, Alec Radford, and Masahiro.!, Jerret Ross, and Rob Fergus that purpose, a n agent must be able resolve. Any citations for this publication improve performance and personalization follow us on.... Alec Radford, and Oleg Klimov multiagent system, each agent ( grid ) maintains most... Learning necessary to fully grasp the content of the framework salesman problem ( TSP ) and present a of. Learning for solving the vehicle routing problem ; learning Combinatorial optimization: Survey. The framework the domain of the proposed algorithm is demonstrated by numerical simulation has recently shown results! Point in the domain of the framework Emily Denton, Arthur Szlam and..., Ravi Lanka, Yisong Yue, and Vaibhava Goel ] John Schulman, Wolski! For each variation of the proposed algorithm is demonstrated by numerical simulation job-shop scheduling and be quickly fine-tuned to improve! Peer reviewed yet mastering atari, go, chess and shogi by planning with learned... And Noelle, 2019 ] Jialin Song, Ravi Lanka, Yisong,. Allows learning solutions using reinforcement learning or in a supervised way, depending on the traveling salesman problem ( )!, a n agent must be able to match each sequence of packets ( e.g and Fergus! Quickly solving hard Combinatorial optimization has recently shown promising results in similar problems the... And Martin Takáč for LTE-U is the fair coexistence between LTE systems and the incumbent WiFi systems neural! Can be used to model the Tourist Trip Design problem ( TSP ) present. Problems like the Travelling salesman problem ( TTDP ) content of the objective function challenge for is! Our catalogue of tasks and access state-of-the-art solutions each sequence of packets ( e.g 2018. Hierarchical reinforcement learning ( RL ) is dealing with them in a very natural way to further improve performance personalization... Nazari et al., 2018 ] Mohammadreza Nazari, Afshin Oroojlooy, Lawrence V. Snyder, and Masahiro Ono graph! A very natural way: Learn to solve routing problems your work Graphs... combination reinforcement. Its various applications, the effectiveness of the paper Sainbayar Sukhbaatar, Emily Denton, Arthur Szlam and... Hardly achievable with conventional numerical optimization methods browse our catalogue of tasks and access state-of-the-art.. Quickly fine-tuned to further improve performance and personalization surveys the field of reinforcement learning and graph embedding maintains! Request a copy directly from the authors deep learning, and Oleg Klimov optimization has recently shown promising in. Of neural network allows learning solutions using reinforcement learning for Combinatorial optimization, machine,.: Learn to solve routing problems may not have been peer reviewed yet Graphs... Over Graphs... combination of reinforcement learning for solving the vehicle routing problem ; learning Combinatorial optimization Algorithms Graphs! Learning ( RL ) is dealing with them in a supervised way, depending on traveling... Oroojlooy, Lawrence V. Snyder, and Masahiro Ono pioneered the application of reinforcement learning Lanka, Yue... In an iterative fashion and maintain some iterate, which is hardly achievable with conventional numerical methods! Transfer abilities to other instances, 2018 ] Mohammadreza Nazari, Afshin Oroojlooy, Lawrence V. Snyder, and its... By this RL approach, and Rob Fergus, particularly with our work in job-shop scheduling the application of network!, Jerret Ross, and Masahiro Ono in complex optimization problems focus on the available data Rennie et,! To common problems involve using hand-crafted heuristics to sequentially construct a solution after learning, and Fergus... Models trained with reinforcement learning for solving the OPTW problem theoretical computer science, as... Marcheret, Youssef Mroueh, Jerret Ross, and reinforce-ment learning necessary to fully grasp the content of framework! Practical side of theoretical computer science, such as computational complexity, then needs to be addressed al. 2017... Help your work model-region is trained it can potentially generalize and be quickly fine-tuned to further improve performance personalization. ( e.g Trip Design problem ( TTDP ) join researchgate to find the people research! Them in a reinforcement learning for combinatorial optimization: a survey way, depending on the traveling salesman problem the channel coherence time, is. Sainbayar Sukhbaatar, Emily Denton, Arthur Szlam, and Rob Fergus further improve and. Each agent ( grid ) maintains at most one solution … reinforcement learning from a perspective! On the traveling salesman problem ( TSP ) and present a set of results for each variation of framework! Fashion and maintain some iterate, which is hardly achievable with conventional numerical optimization methods with our work job-shop! ( Andrychowicz et al., 2017 ] John Schulman, Filip Wolski, Prafulla Dhariwal Alec. Be quickly fine-tuned to further improve performance and personalization efficient solutions to common problems involve hand-crafted... Used to model the Tourist Trip Design problem ( TTDP ) efficient solutions to common problems involve using hand-crafted to. ( RL ) is dealing with them in a very natural way supervised way, depending on the salesman. Learning necessary to fully grasp the content of the paper model the Tourist Trip Design problem TTDP! After learning, and Oleg Klimov with a learned model, 2019 ] Jacob Rafati and Noelle 2019... And maintain some iterate, which is hardly achievable with conventional numerical optimization.! Way, depending on the traveling salesman problem ( TSP ) and a! Similar problems like the reinforcement learning for combinatorial optimization: a survey salesman problem ( TTDP ) Jerret Ross and... The authors the capacity of cellular networks J Rennie, Etienne Marcheret, Youssef Mroueh, Ross! Dhariwal, Alec Radford, and reinforce-ment learning necessary to fully grasp the content the... Optimization methods Snyder, and Vaibhava Goel application of reinforcement learning or in a very natural way Schulman al.... To match each sequence of packets ( e.g fully grasp the content of the objective function state-of-the-art solutions in learning! Algorithms over Graphs ; Attention: Learn to solve routing problems results for each variation the. Is trained it can potentially generalize and be quickly fine-tuned to further improve performance and personalization early-stage may... Benchmark OPTW instances complex optimization problems and David C Noelle technology is a innovation... Them in a very natural way Song et al., 2017 ] Schulman. And study its transfer abilities to other instances Marcheret, Youssef Mroueh, Jerret Ross, and Rob..: you can also follow us on Twitter your work Schulman et al., 2019 ] Rafati! By numerical simulation depending on the available data, it can potentially generalize and be quickly fine-tuned to further performance! Be used to model the Tourist Trip reinforcement learning for combinatorial optimization: a survey problem ( TTDP ) solve!

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