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Reinforcement learning (RL) algorithms have proven to be useful tools for combinatorial optimisation. However, they are still underutilised in facility layout problems (FLPs). At the same time, RL research relies on standardised benchmarks such as the Arcade Learning Environment.

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Publication II: Gym-Flp: A Python Package for Training Reinforcement Learning Algorithms on Facility Layout Problems

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  • 发表日期：

    2026年

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  • Benjamin Heinbach

摘要

Reinforcement learning (RL) algorithms have proven to be useful tools for combinatorial optimisation. However, they are still underutilised in facility layout problems (FLPs). At the same time, RL research relies on standardised benchmarks such as the Arcade Learning Environment.