PROGRESS: Progressive Reinforcement-Learning-Based Surrogate Selection
PROGRESS: Progressive Reinforcement-Learning-Based Surrogate Selection
复制标题
进展：基于渐进强化学习的代理选择
DOI：
10.1007/978-3-642-44973-4_13
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发表时间：
2013
期刊：
影响因子：
--
通讯作者：
B. Bischl
中科院分区：
文献类型：
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作者：
S. Hess;Tobias Wagner;B. Bischl
在大多数工程问题中，评估不同设置的性能的实验既耗时又昂贵，甚至两者兼而有之。因此，序贯试验设计已成为优化这些问题目标函数不可缺少的技术。在这种情况下，大多数问题可以被认为是一个黑箱。具体地说，没有先验已知的函数属性来选择最适合的代理模型类。因此，我们提出了一种新的基于集成的方法，该方法能够利用强化学习技术在优化过程中识别最佳代理模型。该过程是通用的，可以应用于任意集合的代理模型。在24个著名的黑盒函数上的结果表明，渐进过程能够从集成中选择合适的模型，并且它可以与最新的序贯优化方法竞争。
In most engineering problems, experiments for evaluating the performance of different setups are time consuming, expensive, or even both. Therefore, sequential experimental designs have become an indispensable technique for optimizing the objective functions of these problems. In this context, most of the problems can be considered as a black-box. Specifically, no function properties are known a priori to select the best suited surrogate model class. Therefore, we propose a new ensemble-based approach, which is capable of identifying the best surrogate model during the optimization process by using reinforcement learning techniques. The procedure is general and can be applied to arbitrary ensembles of surrogate models. Results are provided on 24 well-known black-box functions to show that the progressive procedure is capable of selecting suitable models from the ensemble and that it can compete with state-of-the-art methods for sequential optimization.