用能量模型+强化学习改进黑盒优化，避免陷入局部最优
Optimizing the Unknown: Black Box Bayesian Optimization with Energy-Based Model and Reinforcement Learning
- 将每轮优化建模为马尔可夫决策过程，用PPO实现多步前瞻探索
- 在合成与真实任务上优于传统方法，高维复杂场景表现更稳定
- 适合函数不可导、评估代价高的优化问题，如超参调优或实验设计
现有贝叶斯优化方法通常在探索与利用间权衡以优化高成本目标函数，但常因一阶偏差导致收敛至局部最优，在复杂或高维任务中表现不佳。近年来，黑盒优化在科学与工程领域取得成功，尤其适用于函数评估代价高且无梯度信息的场景。为此，我们提出基于强化的能量模型贝叶斯优化（REBMBO），结合高斯过程（GP）提供局部引导，并引入能量模型（EBM）捕捉全局结构信息。特别地，我们将每轮贝叶斯优化视为马尔可夫决策过程（MDP），采用近端策略优化（PPO）实现自适应多步前瞻，动态调整探索深度与方向，有效克服传统方法局限。我们在合成数据与真实世界基准上进行了广泛实验，验证了REBMBO的优越性能。进一步分析不同GP配置下的表现，凸显其适应性与鲁棒性。
原文摘要 · Abstract (English)
Existing Bayesian Optimization (BO) methods typically balance exploration and exploitation to optimize costly objective functions. However, these methods often suffer from a significant one-step bias, which may lead to convergence towards local optima and poor performance in complex or high-dimensional tasks. Recently, Black-Box Optimization (BBO) has achieved success across various scientific and engineering domains, particularly when function evaluations are costly and gradients are unavailable. Motivated by this, we propose the Reinforced Energy-Based Model for Bayesian Optimization (REBMBO), which integrates Gaussian Processes (GP) for local guidance with an Energy-Based Model (EBM) to capture global structural information. Notably, we define each Bayesian Optimization iteration as a Markov Decision Process (MDP) and use Proximal Policy Optimization (PPO) for adaptive multi-step lookahead, dynamically adjusting the depth and direction of exploration to effectively overcome the limitations of traditional BO methods. We conduct extensive experiments on synthetic and real-world benchmarks, confirming the superior performance of REBMBO. Additional analyses across various GP configurations further highlight its adaptability and robustness.
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