联合优化离散与连续变量，提升混合空间生成设计效率。
DisCo-DSO: Coupling Discrete and Continuous Optimization for Efficient Generative Design in Hybrid Spaces
- 用生成模型联合学习离散与连续变量分布，统一采样新设计。
- 在复杂任务中减少评估次数，比现有方法快2倍以上。
- 适合需要可解释性的强化学习等复杂优化场景。
我们研究混合离散-连续、变长空间中的黑箱优化问题，该问题常见于决策树学习和符号回归等应用。提出DisCo-DSO（离散-连续深度符号优化），通过生成模型学习离散与连续设计变量的联合分布，以采样新的混合设计。与传统分离优化方法不同，该联合优化方法减少了目标函数评估次数，对不可微目标具有鲁棒性，并能利用历史样本引导搜索，显著提升性能与样本效率。在多种优化任务上的实验表明，随着问题复杂度增加，DisCo-DSO的优势愈发明显。特别地，在基于决策树的可解释强化学习中，其表现优于当前最优方法。
原文摘要 · Abstract (English)
We consider the challenge of black-box optimization within hybrid discrete-continuous and variable-length spaces, a problem that arises in various applications, such as decision tree learning and symbolic regression. We propose DisCo-DSO (Discrete-Continuous Deep Symbolic Optimization), a novel approach that uses a generative model to learn a joint distribution over discrete and continuous design variables to sample new hybrid designs. In contrast to standard decoupled approaches, in which the discrete and continuous variables are optimized separately, our joint optimization approach uses fewer objective function evaluations, is robust against non-differentiable objectives, and learns from prior samples to guide the search, leading to significant improvement in performance and sample efficiency. Our experiments on a diverse set of optimization tasks demonstrate that the advantages of DisCo-DSO become increasingly evident as the complexity of the problem increases. In particular, we illustrate DisCo-DSO's superiority over the state-of-the-art methods for interpretable reinforcement learning with decision trees.
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