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Ecole: A Gym-like Library for Machine Learning in Combinatorial Optimization Solvers
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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.
Forward citations
Cited by 3 Pith papers
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GraphBU: MILP Instance Generation with Graph-Native Block Units
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ReviBranch trains a reinforcement learning branching policy for MILP solvers on revived historical trajectories with densified rewards, reporting 4.0% fewer search nodes and 2.2% fewer LP iterations on large instances.
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STRCMP: Integrating Graph Structural Priors with Language Models for Combinatorial Optimization
STRCMP's GNN-plus-LLM code search for MILP and SAT heuristics does not consistently beat AutoSAT in the paper's own reported numbers.
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