{"total":170,"rows":[{"id":"2102.09544","title":"Combinatorial optimization and reasoning with graph neural networks","created":"2021-02-18","cited_by_count":268,"area":null},{"id":"2102.11756","title":"Deep Policy Dynamic Programming for Vehicle Routing Problems","created":"2021-02-23","cited_by_count":94,"area":null},{"id":"2312.08224","title":"GLOP: Learning Global Partition and Local Construction for Solving Large-scale Routing Problems in Real-time","created":"2023-12-13","cited_by_count":85,"area":null},{"id":"2301.03313","title":"BQ-NCO: Bisimulation Quotienting for Efficient Neural Combinatorial Optimization","created":"2023-01-09","cited_by_count":82,"area":null},{"id":"2309.14032","title":"DeepACO: Neural-enhanced Ant Systems for Combinatorial Optimization","created":"2023-09-25","cited_by_count":78,"area":null},{"id":"2306.17100","title":"RL4CO: an Extensive Reinforcement Learning for Combinatorial Optimization Benchmark","created":"2023-06-29","cited_by_count":71,"area":null},{"id":"2210.03475","title":"Winner Takes It All: Training Performant RL Populations for Combinatorial Optimization","created":"2022-10-07","cited_by_count":58,"area":null},{"id":"2203.15386","title":"Pareto Set Learning for Neural Multi-objective Combinatorial Optimization","created":"2022-03-29","cited_by_count":56,"area":null},{"id":"2406.15007","title":"RouteFinder: Towards Foundation Models for Vehicle Routing Problems","created":"2024-06-21","cited_by_count":55,"area":null},{"id":"2407.00379","title":"GraphArena: Evaluating and Exploring Large Language Models on Graph Computation","created":"2024-06-29","cited_by_count":49,"area":null},{"id":"2104.14516","title":"Constructions in combinatorics via neural networks","created":"2021-04-29","cited_by_count":48,"area":null},{"id":"2101.08543","title":"Boost then Convolve: Gradient Boosting Meets Graph Neural Networks","created":"2021-01-21","cited_by_count":44,"area":null},{"id":"2311.13569","title":"Combinatorial Optimization with Policy Adaptation using Latent Space Search","created":"2023-11-13","cited_by_count":41,"area":null},{"id":"2209.12288","title":"On Representing Linear Programs by Graph Neural Networks","created":"2022-09-25","cited_by_count":39,"area":null},{"id":"2310.06116","title":"OptiMUS: Optimization Modeling Using MIP Solvers and large language models","created":"2023-10-09","cited_by_count":39,"area":null},{"id":"2106.03051","title":"ScheduleNet: Learn to solve multi-agent scheduling problems with reinforcement learning","created":"2021-06-06","cited_by_count":31,"area":null},{"id":"2206.07922","title":"Challenges and Opportunities in Deep Reinforcement Learning with Graph Neural Networks: A Comprehensive review of Algorithms and Applications","created":"2022-06-16","cited_by_count":30,"area":null},{"id":"2308.08235","title":"The Expressive Power of Graph Neural Networks: A Survey","created":"2023-08-16","cited_by_count":27,"area":null},{"id":"2401.11849","title":"Self-Labeling the Job Shop Scheduling Problem","created":"2024-01-22","cited_by_count":23,"area":null},{"id":"2406.00415","title":"Neural Combinatorial Optimization Algorithms for Solving Vehicle Routing Problems: A Comprehensive Survey with Perspectives","created":"2024-06-01","cited_by_count":23,"area":null},{"id":"2206.02568","title":"A Deep Reinforcement Learning Framework For Column Generation","created":"2022-06-03","cited_by_count":22,"area":null},{"id":"2206.10121","title":"Finite Expression Method for Solving High-Dimensional Partial Differential Equations","created":"2022-06-21","cited_by_count":21,"area":null},{"id":"2210.12547","title":"SurCo: Learning Linear Surrogates For Combinatorial Nonlinear Optimization Problems","created":"2022-10-22","cited_by_count":21,"area":null},{"id":"2310.15196","title":"Efficient Meta Neural Heuristic for Multi-Objective Combinatorial Optimization","created":"2023-10-22","cited_by_count":21,"area":null},{"id":"2311.14156","title":"Variational Annealing on Graphs for Combinatorial Optimization","created":"2023-11-23","cited_by_count":21,"area":null},{"id":"2105.02741","title":"Meta-Learning-Based Deep Reinforcement Learning for Multiobjective Optimization Problems","created":"2021-05-06","cited_by_count":20,"area":null},{"id":"2301.03116","title":"Unsupervised Learning for Combinatorial Optimization Needs Meta-Learning","created":"2023-01-08","cited_by_count":20,"area":null},{"id":"2302.09166","title":"Machine Learning for Cutting Planes in Integer Programming: A Survey","created":"2023-02-17","cited_by_count":20,"area":null},{"id":"2206.13181","title":"Learning to Control Local Search for Combinatorial Optimization","created":"2022-06-27","cited_by_count":18,"area":null},{"id":"2203.11203","title":"Reinforcement learning for automatic quadrilateral mesh generation: a soft actor-critic approach","created":"2022-03-19","cited_by_count":17,"area":null},{"id":"2109.13983","title":"Guidelines for the Computational Testing of Machine Learning approaches to Vehicle Routing Problems","created":"2021-09-28","cited_by_count":16,"area":null},{"id":"2205.02453","title":"Learning to Solve Vehicle Routing Problems: A Survey","created":"2022-05-05","cited_by_count":16,"area":null},{"id":"2309.16965","title":"Controlling Continuous Relaxation for Combinatorial Optimization","created":"2023-09-29","cited_by_count":16,"area":null},{"id":"2402.01874","title":"The RL/LLM Taxonomy Tree: Reviewing Synergies Between Reinforcement Learning and Large Language Models","created":"2024-02-02","cited_by_count":15,"area":null},{"id":"2112.12545","title":"A Deep Reinforcement Learning Approach for Solving the Traveling Salesman Problem with Drone","created":"2021-12-22","cited_by_count":14,"area":null},{"id":"2205.14105","title":"Learning to Solve Combinatorial Graph Partitioning Problems via Efficient Exploration","created":"2022-05-27","cited_by_count":14,"area":null},{"id":"2208.04055","title":"Neural Set Function Extensions: Learning with Discrete Functions in High Dimensions","created":"2022-08-08","cited_by_count":14,"area":null},{"id":"2404.01574","title":"Multi-granular Adversarial Attacks against Black-box Neural Ranking Models","created":"2024-04-02","cited_by_count":14,"area":null},{"id":"2102.05875","title":"Deep Reinforcement Learning for Combinatorial Optimization: Covering Salesman Problems","created":"2021-02-11","cited_by_count":13,"area":null},{"id":"2310.15195","title":"Neural Multi-Objective Combinatorial Optimization with Diversity Enhancement","created":"2023-10-22","cited_by_count":13,"area":null},{"id":"2206.06965","title":"Deep Reinforcement Learning for Exact Combinatorial Optimization: Learning to Branch","created":"2022-06-14","cited_by_count":12,"area":null},{"id":"2307.05979","title":"Transformers in Reinforcement Learning: A Survey","created":"2023-07-12","cited_by_count":12,"area":null},{"id":"2210.07500","title":"ToupleGDD: A Fine-Designed Solution of Influence Maximization by Deep Reinforcement Learning","created":"2022-10-14","cited_by_count":11,"area":null},{"id":"2509.08269","title":"A Systematic Survey on Large Language Models for Evolutionary Optimization: From Modeling to Solving","created":"2025-09-10","cited_by_count":11,"area":null},{"id":"2109.10380","title":"Deep Policies for Online Bipartite Matching: A Reinforcement Learning Approach","created":"2021-09-21","cited_by_count":10,"area":null},{"id":"2206.05060","title":"Social Network Structure Shapes Innovation: Experience-sharing in RL with SAPIENS","created":"2022-06-10","cited_by_count":10,"area":null},{"id":"2104.01992","title":"Qubit Routing using Graph Neural Network aided Monte Carlo Tree Search","created":"2021-04-01","cited_by_count":9,"area":null},{"id":"2201.05000","title":"Automated Reinforcement Learning: An Overview","created":"2022-01-13","cited_by_count":9,"area":null},{"id":"2103.02649","title":"Self-play Learning Strategies for Resource Assignment in Open-RAN Networks","created":"2021-03-03","cited_by_count":8,"area":null},{"id":"2104.03546","title":"Graph Partitioning and Sparse Matrix Ordering using Reinforcement Learning and Graph Neural Networks","created":"2021-04-08","cited_by_count":8,"area":null},{"id":"2107.07961","title":"MODRL/D-EL: Multiobjective Deep Reinforcement Learning with Evolutionary Learning for Multiobjective Optimization","created":"2021-07-16","cited_by_count":8,"area":null},{"id":"2205.10106","title":"LeNSE: Learning To Navigate Subgraph Embeddings for Large-Scale Combinatorial Optimisation","created":"2022-05-20","cited_by_count":8,"area":null},{"id":"2211.09382","title":"Planning Irregular Object Packing via Hierarchical Reinforcement Learning","created":"2022-11-17","cited_by_count":8,"area":null},{"id":"2308.13420","title":"Reinforcement Learning-assisted Evolutionary Algorithm: A Survey and Research Opportunities","created":"2023-08-25","cited_by_count":8,"area":null},{"id":"2310.03195","title":"Deep reinforcement learning for machine scheduling: Methodology, the state-of-the-art, and future directions","created":"2023-10-04","cited_by_count":8,"area":null},{"id":"2303.02442","title":"Neural Airport Ground Handling","created":"2023-03-04","cited_by_count":7,"area":null},{"id":"2405.08424","title":"Tackling Prevalent Conditions in Unsupervised Combinatorial Optimization: Cardinality, Minimum, Covering, and More","created":"2024-05-14","cited_by_count":7,"area":null},{"id":"2409.03811","title":"PARCO: Parallel AutoRegressive Models for Multi-Agent Combinatorial Optimization","created":"2024-09-05","cited_by_count":7,"area":null},{"id":"2507.00218","title":"Learning for routing: A guided review of recent developments and future directions","created":"2025-06-30","cited_by_count":7,"area":null},{"id":"2205.00897","title":"Fast Continuous and Integer L-shaped Heuristics Through Supervised Learning","created":"2022-05-02","cited_by_count":6,"area":null},{"id":"2211.08179","title":"Artificial intelligence approaches for materials-by-design of energetic materials: state-of-the-art, challenges, and future directions","created":"2022-11-15","cited_by_count":6,"area":null},{"id":"2309.13851","title":"DISeR: Designing Imaging Systems with Reinforcement Learning","created":"2023-09-25","cited_by_count":6,"area":null},{"id":"2309.17089","title":"Too Big, so Fail? -- Enabling Neural Construction Methods to Solve Large-Scale Routing Problems","created":"2023-09-29","cited_by_count":6,"area":null},{"id":"2402.14847","title":"Deep learning-driven scheduling algorithm for a single machine problem minimizing the total tardiness","created":"2024-02-19","cited_by_count":6,"area":null},{"id":"2109.07498","title":"Short Quantum Circuits in Reinforcement Learning Policies for the Vehicle Routing Problem","created":"2021-09-15","cited_by_count":5,"area":null},{"id":"2111.06726","title":"One model Packs Thousands of Items with Recurrent Conditional Query Learning","created":"2021-11-12","cited_by_count":5,"area":null},{"id":"2204.09052","title":"Optimizing Tensor Network Contraction Using Reinforcement Learning","created":"2022-04-18","cited_by_count":5,"area":null},{"id":"2305.05812","title":"Assessment of Reinforcement Learning Algorithms for Nuclear Power Plant Fuel Optimization","created":"2023-05-09","cited_by_count":5,"area":null},{"id":"2310.05309","title":"Optimizing Solution-Samplers for Combinatorial Problems: The Landscape of Policy-Gradient Methods","created":"2023-10-08","cited_by_count":5,"area":null},{"id":"2310.18672","title":"Maximum Independent Set: Self-Training through Dynamic Programming","created":"2023-10-28","cited_by_count":5,"area":null},{"id":"2312.10194","title":"Multi-Objective Reinforcement Learning-based Approach for Pressurized Water Reactor Optimization","created":"2023-12-15","cited_by_count":5,"area":null},{"id":"2405.16951","title":"Fast ML-driven Analog Circuit Layout using Reinforcement Learning and Steiner Trees","created":"2024-05-27","cited_by_count":5,"area":null},{"id":"2411.15212","title":"Effective Analog ICs Floorplanning with Relational Graph Neural Networks and Reinforcement Learning","created":"2024-11-20","cited_by_count":5,"area":null},{"id":"2106.03157","title":"Self-Supervision is All You Need for Solving Rubik's Cube","created":"2021-06-06","cited_by_count":4,"area":null},{"id":"2206.00383","title":"Neural Improvement Heuristics for Graph Combinatorial Optimization Problems","created":"2022-06-01","cited_by_count":4,"area":null},{"id":"2206.01812","title":"Challenges to Solving Combinatorially Hard Long-Horizon Deep RL Tasks","created":"2022-06-03","cited_by_count":4,"area":null},{"id":"2308.13270","title":"A Game of Bundle Adjustment -- Learning Efficient Convergence","created":"2023-08-25","cited_by_count":4,"area":null},{"id":"2310.11787","title":"NeuroCUT: A Neural Approach for Robust Graph Partitioning","created":"2023-10-18","cited_by_count":4,"area":null},{"id":"2312.08103","title":"Machine Learning for the Multi-Dimensional Bin Packing Problem: Literature Review and Empirical Evaluation","created":"2023-12-13","cited_by_count":4,"area":null},{"id":"2312.14213","title":"A Reinforcement-Learning-Based Multiple-Column Selection Strategy for Column Generation","created":"2023-12-21","cited_by_count":4,"area":null},{"id":"2404.12356","title":"Improving the interpretability of GNN predictions through conformal-based graph sparsification","created":"2024-04-18","cited_by_count":4,"area":null},{"id":"2406.16424","title":"Memory-Enhanced Neural Solvers for Routing Problems","created":"2024-06-24","cited_by_count":4,"area":null},{"id":"2407.16468","title":"Enhancing GNNs Performance on Combinatorial Optimization by Recurrent Feature Update","created":"2024-07-23","cited_by_count":4,"area":null},{"id":"2602.21761","title":"Survey on Neural Routing Solvers","created":"2026-02-25","cited_by_count":4,"area":null},{"id":"2107.10901","title":"A reinforcement learning approach to resource allocation in genomic selection","created":"2021-07-22","cited_by_count":3,"area":null},{"id":"2108.04462","title":"Deep Reinforcement Learning for Demand Driven Services in Logistics and Transportation Systems: A Survey","created":"2021-08-10","cited_by_count":3,"area":null},{"id":"2111.09191","title":"Preference Communication in Multi-Objective Normal-Form Games","created":"2021-11-17","cited_by_count":3,"area":null},{"id":"2301.01913","title":"Learning a Generic Value-Selection Heuristic Inside a Constraint Programming Solver","created":"2023-01-05","cited_by_count":3,"area":null},{"id":"2307.04962","title":"Intrinsically motivated graph exploration using network theories of human curiosity","created":"2023-07-11","cited_by_count":3,"area":null},{"id":"2310.12387","title":"Learning to Optimise Climate Sensor Placement using a Transformer","created":"2023-10-18","cited_by_count":3,"area":null},{"id":"2310.15706","title":"Solving the flexible job-shop scheduling problem through an enhanced deep reinforcement learning approach","created":"2023-10-24","cited_by_count":3,"area":null},{"id":"2402.03640","title":"torchmSAT: A GPU-Accelerated Approximation To The Maximum Satisfiability Problem","created":"2024-02-06","cited_by_count":3,"area":null},{"id":"2408.02207","title":"MARCO: A Memory-Augmented Reinforcement Framework for Combinatorial Optimization","created":"2024-08-05","cited_by_count":3,"area":null},{"id":"2101.04167","title":"First-Order Problem Solving through Neural MCTS based Reinforcement Learning","created":"2021-01-11","cited_by_count":2,"area":null},{"id":"2108.10224","title":"A New Constructive Heuristic driven by Machine Learning for the Traveling Salesman Problem","created":"2021-08-17","cited_by_count":2,"area":null},{"id":"2202.05423","title":"Understanding Curriculum Learning in Policy Optimization for Online Combinatorial Optimization","created":"2022-02-11","cited_by_count":2,"area":null},{"id":"2209.04265","title":"Route Planning for Last-Mile Deliveries Using Mobile Parcel Lockers: A Hybrid Q-Learning Network Approach","created":"2022-09-09","cited_by_count":2,"area":null},{"id":"2209.04698","title":"Structured Q-learning For Antibody Design","created":"2022-09-10","cited_by_count":2,"area":null},{"id":"2209.09983","title":"Deep-Steiner: Learning to Solve the Euclidean Steiner Tree Problem","created":"2022-09-20","cited_by_count":2,"area":null},{"id":"2306.09633","title":"The False Dawn: Reevaluating Google's Reinforcement Learning for Chip Macro Placement","created":"2023-06-16","cited_by_count":2,"area":null}],"next_cursor":"100"}