Monte Carlo Tree Descent for Black-Box OptimizationDownload PDF

Published: 31 Oct 2022, Last Modified: 16 Oct 2022NeurIPS 2022 AcceptReaders: Everyone
Keywords: Monte Carlo tree search, blackbox optimization, stochastic search, Bayesian optimization
Abstract: The key to Black-Box Optimization is to efficiently search through input regions with potentially widely-varying numerical properties, to achieve low-regret descent and fast progress toward the optima. Monte Carlo Tree Search (MCTS) methods have recently been introduced to improve Bayesian optimization by computing better partitioning of the search space that balances exploration and exploitation. Extending this promising framework, we study how to further integrate sample-based descent for faster optimization. We design novel ways of expanding Monte Carlo search trees, with new descent methods at vertices that incorporate stochastic search and Gaussian Processes. We propose the corresponding rules for balancing progress and uncertainty, branch selection, tree expansion, and backpropagation. The designed search process puts more emphasis on sampling for faster descent and uses localized Gaussian Processes as auxiliary metrics for both exploitation and exploration. We show empirically that the proposed algorithms can outperform state-of-the-art methods on many challenging benchmark problems.
TL;DR: We propose new Monte Carlo Tree Search methods that balance local descent and Bayesian optimization for black-box optimization problems.
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