an Extensive Reinforcement Learning for Combinatorial Optimization Benchmark

View the PDF file from the paper entitled RL4CO: Learn to promote a wide range of consensual improvement standards, by Federico Berto and 32 other author

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a summary:Co -improvement (CO) is essential in many real -world applications, from logistical services and schedule to devices design and resource customization. Deep learning (RL) recently showed great benefits in solving carbon dioxide problems, which reduces dependence on field experience and improve mathematical efficiency. However, the absence of a frame for a unified measurement leads to inconvenient assessments, leads to cloning, and increases engineering general expenditures, which raises barriers in front of the adoption of new researchers. To face these challenges, we offer RL4CO, a unified and comprehensive standard with an in -depth library coverage of 27 CO and 23 modern lines. Building on active software libraries and best practices in implementation, RL4CO features a normative implementation and flexible configurations of various environments, policy structures, RL algorithms, and facilities with extensive documents. RL4CO helps researchers build current successes while exploring and developing their own designs, facilitating the entire search process by separating science from heavy engineering. We finally provide extensive standard studies to inspire new visions and future action. RL4CO has already attracted many researchers in society and is open source in this URL https.