# Ecole: A Gym-like Library for Machine Learning in Combinatorial Optimization Solvers

arXiv: https://arxiv.org/abs/2011.06069 · HTML: https://mlsanity.com/paper/2011.06069/

## Key facts

- Cited by 43 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Nov 2020.

## Built on by (most cited first, 30 of 43)

- [Combinatorial optimization and reasoning with graph neural networks](https://mlsanity.com/paper/2102.09544/) (2021)
- [RL4CO: an Extensive Reinforcement Learning for Combinatorial Optimization Benchmark](https://mlsanity.com/paper/2306.17100/) (2023)
- [Learning To Cut By Looking Ahead: Cutting Plane Selection via Imitation Learning](https://mlsanity.com/paper/2206.13414/) (2022)
- [RouteFinder: Towards Foundation Models for Vehicle Routing Problems](https://mlsanity.com/paper/2406.15007/) (2024)
- [Learning to branch with Tree MDPs](https://mlsanity.com/paper/2205.11107/) (2022)
- [A Survey for Solving Mixed Integer Programming via Machine Learning](https://mlsanity.com/paper/2203.02878/) (2022)
- [The Machine Learning for Combinatorial Optimization Competition (ML4CO): Results and Insights](https://mlsanity.com/paper/2203.02433/) (2022)
- [Searching Large Neighborhoods for Integer Linear Programs with Contrastive Learning](https://mlsanity.com/paper/2302.01578/) (2023)
- [Towards Foundation Models for Mixed Integer Linear Programming](https://mlsanity.com/paper/2410.08288/) (2024)
- [Machine Learning Augmented Branch and Bound for Mixed Integer Linear Programming](https://mlsanity.com/paper/2402.05501/) (2024)
- [A Deep Instance Generative Framework for MILP Solvers Under Limited Data Availability](https://mlsanity.com/paper/2310.02807/) (2023)
- [Reinforcement Learning for Branch-and-Bound Optimisation using Retrospective Trajectories](https://mlsanity.com/paper/2205.14345/) (2022)
- [MILP-StuDio: MILP Instance Generation via Block Structure Decomposition](https://mlsanity.com/paper/2410.22806/) (2024)
- [Distributional MIPLIB: a Multi-Domain Library for Advancing ML-Guided MILP Methods](https://mlsanity.com/paper/2406.06954/) (2024)
- [An Improved Reinforcement Learning Algorithm for Learning to Branch](https://mlsanity.com/paper/2201.06213/) (2022)
- [Learning to Configure Separators in Branch-and-Cut](https://mlsanity.com/paper/2311.05650/) (2023)
- [Deep Reinforcement Learning for Exact Combinatorial Optimization: Learning to Branch](https://mlsanity.com/paper/2206.06965/) (2022)
- [Promoting Generalization for Exact Solvers via Adversarial Instance Augmentation](https://mlsanity.com/paper/2310.14161/) (2023)
- [Learning to Cut via Hierarchical Sequence/Set Model for Efficient Mixed-Integer Programming](https://mlsanity.com/paper/2404.12638/) (2024)
- [Learning to Stop Cut Generation for Efficient Mixed-Integer Linear Programming](https://mlsanity.com/paper/2401.17527/) (2024)
- [Effective Generation of Feasible Solutions for Integer Programming via Guided Diffusion](https://mlsanity.com/paper/2406.12349/) (2024)
- [DOGE-Train: Discrete Optimization on GPU with End-to-end Training](https://mlsanity.com/paper/2205.11638/) (2022)
- [Automatic MILP Solver Configuration By Learning Problem Similarities](https://mlsanity.com/paper/2307.00670/) (2023)
- [LLM4Branch: Large Language Model for Discovering Efficient Branching Policies of Integer Programs](https://mlsanity.com/paper/2605.10401/) (2026)
- [Exact Combinatorial Optimization with Temporo-Attentional Graph Neural Networks](https://mlsanity.com/paper/2311.13843/) (2023)
- [STRCMP: Integrating Graph Structural Priors with Language Models for Combinatorial Optimization](https://mlsanity.com/paper/2506.11057/) (2025)
- [Yordle: An Efficient Imitation Learning for Branch and Bound](https://mlsanity.com/paper/2202.01896/) (2022)
- [Are Your Generated Instances Truly Useful? GenBench-MILP: A Benchmark Suite for MILP Instance Generation](https://mlsanity.com/paper/2505.24779/) (2025)
- [Planning in Branch-and-Bound: Model-Based Reinforcement Learning for Exact Combinatorial Optimization](https://mlsanity.com/paper/2511.09219/) (2025)
- [TreeDQN: Sample-Efficient Off-Policy Reinforcement Learning for Combinatorial Optimization](https://mlsanity.com/paper/2306.05905/) (2023)

## Abstract (arXiv)

We present Ecole, a new library to simplify machine learning research for combinatorial optimization. Ecole exposes several key decision tasks arising in general-purpose combinatorial optimization solvers as control problems over Markov decision processes. Its interface mimics the popular OpenAI Gym library and is both extensible and intuitive to use. We aim at making this library a standardized platform that will lower the bar of entry and accelerate innovation in the field. Documentation and code can be found at https://www.ecole.ai.