Reinforced In-Context Black-Box Optimization
<|reference_start|>Reinforced In-Context Black-Box Optimization: Black-Box Optimization (BBO) has found successful applications in many fields of science and engineering. Recently, there has been a growing interest in meta-learning particular components of BBO algorithms to speed up optimization and get rid of tedious hand-crafted heuristics. As an extension, learning the entire algorithm from data requires the least labor from experts and can provide the most flexibility. In this paper, we propose RIBBO, a method to reinforce-learn a BBO algorithm from offline data in an end-to-end fashion. RIBBO employs expressive sequence models to learn the optimization histories produced by multiple behavior algorithms and tasks, leveraging the in-context learning ability of large models to extract task information and make decisions accordingly. Central to our method is to augment the optimization histories with \textit{regret-to-go} tokens, which are designed to represent the performance of an algorithm based on cumulative regret over the future part of the histories. The integration of regret-to-go tokens enables RIBBO to automatically generate sequences of query points that satisfy the user-desired regret, which is verified by its universally good empirical performance on diverse problems, including BBO benchmark functions, hyper-parameter optimization and robot control problems.<|reference_end|>
@article{song2024reinforced,
    title={Reinforced In-Context Black-Box Optimization},
    author={Lei Song, Chenxiao Gao, Ke Xue, Chenyang Wu, Dong Li, Jianye Hao,
  Zongzhang Zhang, Chao Qian},
    journal={arXiv preprint arXiv:2402.17423},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.17423},
    primaryClass={cs.LG cs.AI cs.NE}
}