BOFormer: Learning to Solve Multi-Objective Bayesian Optimization via Non-Markovian RL

Understand

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 \textit{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 \textit{BOFormer}, which substantiates this framework for MOBO via sequence modeling.