mlsanityData to 14 Oct 2026

RLOR: A Flexible Framework of Deep Reinforcement Learning for Operation Research

arXiv 2303.13117 ↗First posted 23 Mar 2023math.OC cs.LG cs.NEcited by 4

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Abstract (arXiv)

Reinforcement learning has been applied in operation research and has shown promise in solving large combinatorial optimization problems. However, existing works focus on developing neural network architectures for certain problems. These works lack the flexibility to incorporate recent advances in reinforcement learning, as well as the flexibility of customizing model architectures for operation research problems. In this work, we analyze the end-to-end autoregressive models for vehicle routing problems and show that these models can benefit from the recent advances in reinforcement learning with a careful re-implementation of the model architecture. In particular, we re-implemented the Attention Model and trained it with Proximal Policy Optimization (PPO) in CleanRL, showing at least 8 times speed up in training time. We hereby introduce RLOR, a flexible framework for Deep Reinforcement Learning for Operation Research. We believe that a flexible framework is key to developing deep reinforcement learning models for operation research problems. The code of our work is publicly available at https://github.com/cpwan/RLOR.

Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Builds on 20 of its 31 references that are in the map. First posted on arXiv 23 Mar 2023.

Builds on

20 of 31 references.

References · 20

  1. 2021Decision Transformer: Reinforcement Learning via Sequence Modelingcited by 1,546
  2. 2015Pointer Networkscited by 895
  3. 2018Attention, Learn to Solve Routing Problems!cited by 656
  4. 2016Neural Combinatorial Optimization with Reinforcement Learningcited by 608
  5. 2017Learning Combinatorial Optimization Algorithms over Graphscited by 561
  6. 2020POMO: Policy Optimization with Multiple Optima for Reinforcement Learningcited by 348
  7. 2018Reinforcement Learning for Solving the Vehicle Routing Problemcited by 333
  8. 2019An Efficient Graph Convolutional Network Technique for the Travelling Salesman Problemcited by 269
  9. 2018Learning to Perform Local Rewriting for Combinatorial Optimizationcited by 185
  10. 2018Combinatorial Optimization with Graph Convolutional Networks and Guided Tree Searchcited by 183
  11. 2020Generalize a Small Pre-trained Model to Arbitrarily Large TSP Instancescited by 162
  12. 2021Tianshou: a Highly Modularized Deep Reinforcement Learning Librarycited by 143
8 more
  1. 2021Learning to Iteratively Solve Routing Problems with Dual-Aspect Collaborative Transformercited by 108
  2. 2021Efficient Active Search for Combinatorial Optimization Problemscited by 100
  3. 2021Deep Policy Dynamic Programming for Vehicle Routing Problemscited by 94
  4. 2019Exploratory Combinatorial Optimization with Reinforcement Learningcited by 82
  5. 2020Combining Reinforcement Learning and Constraint Programming for Combinatorial Optimizationcited by 63
  6. 2020OR-Gym: A Reinforcement Learning Library for Operations Research Problemscited by 51
  7. 2022What's Wrong with Deep Learning in Tree Search for Combinatorial Optimizationcited by 37
  8. 2021Generalization in Deep RL for TSP Problems via Equivariance and Local Searchcited by 9

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 1; 2024: 2; 2025: 0; 2026: 1.

4 papers.

  1. 2023RL4CO: an Extensive Reinforcement Learning for Combinatorial Optimization Benchmarkcited by 71
  2. 2024Learn to Solve Vehicle Routing Problems ASAP: A Neural Optimization Approach for Time-Constrained Vehicle Routing Problems with Finite Vehicle Fleetcited by 0
  3. 2024Multi-Agent Environments for Vehicle Routing Problemscited by 0
  4. 2026Learning to Solve Orienteering Problem with Time Windows and Variable Profitscited by 0