Neural Nonmyopic Bayesian Optimization in Dynamic Cost Settings

TL;DR

LookaHES is a neural nonmyopic Bayesian optimization framework that effectively handles dynamic, history-dependent costs in complex, structured decision spaces, outperforming existing methods in synthetic and real-world tasks.

Contribution

Introduces LookaHES, a scalable neural nonmyopic BO method that incorporates long-horizon planning and neural policies for dynamic cost environments.

Findings

Outperforms myopic and nonmyopic baselines on synthetic benchmarks.

Achieves superior results in geospatial optimization and protein sequence design.

Enables long-horizon planning beyond twenty steps efficiently.

Abstract

Bayesian optimization (BO) is a common framework for optimizing black-box functions, yet most existing methods assume static query costs and rely on myopic acquisition strategies. We introduce LookaHES, a nonmyopic BO framework designed for dynamic, history-dependent cost environments, where evaluation costs vary with prior actions, such as travel distance in spatial tasks or edit distance in sequence design. LookaHES combines a multi-step variant of $H$-Entropy Search with pathwise sampling and neural policy optimization, enabling long-horizon planning beyond twenty steps without the exponential complexity of existing nonmyopic methods. The key innovation is the integration of neural policies, including large language models, to effectively navigate structured, combinatorial action spaces such as protein sequences. These policies amortize lookahead planning and can be integrated with domain-specific constraints during rollout. Empirically, LookaHES outperforms strong myopic and nonmyopic baselines across nine synthetic benchmarks from two to eight dimensions and two real-world tasks: geospatial optimization using NASA night-light imagery and protein sequence design with constrained token-level edits. In short, LookaHES provides a general, scalable, and cost-aware solution for robust long-horizon optimization in complex decision spaces, which makes it a useful tool for researchers in machine learning, statistics, and applied domains. Our implementation is available at https://github.com/sangttruong/nonmyopia.