Keywords: Multi-Objective Bayesian Optimization, Transformers, Hyperparameter Optimization, Reinforcement Learning, Acquisition Function
TL;DR: A non-Markovian RL framework with Transformers for Multi-Objective Bayesian Optimization
Abstract: Bayesian optimization (BO) offers an efficient pipeline for optimizing black-box functions with the help of a Gaussian process prior and an acquisition function (AF). Recently, in the context of single-objective BO, learning-based AFs witnessed promising empirical results given its favorable non-myopic nature. Despite this, the direct extension of these approaches to multi-objective Bayesian optimization (MOBO) suffer from the *hypervolume identifiability issue*, which results from the non-Markovian nature of MOBO problems. To tackle this, inspired by the non-Markovian RL literature and the success of Transformers in language modeling, we present a generalized deep Q-learning framework and propose *BOFormer*, which substantiates this framework for MOBO via sequence modeling. Through extensive evaluation, we demonstrate that BOFormer constantly achieves better performance than the benchmark rule-based and learning-based algorithms in various synthetic MOBO and real-world multi-objective hyperparameter optimization problems.
Supplementary Material: zip
Submission Number: 32
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