Epsilon-Greedy Thompson Sampling to Bayesian Optimization
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arXiv
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| Hauptverfasser: | , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866929568984596480 |
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| author | Do, Bach Adebiyi, Taiwo Zhang, Ruda |
| author_facet | Do, Bach Adebiyi, Taiwo Zhang, Ruda |
| contents | Bayesian optimization (BO) has become a powerful tool for solving simulation-based engineering optimization problems thanks to its ability to integrate physical and mathematical understandings, consider uncertainty, and address the exploitation-exploration dilemma. Thompson sampling (TS) is a preferred solution for BO to handle the exploitation-exploration trade-off. While it prioritizes exploration by generating and minimizing random sample paths from probabilistic models -- a fundamental ingredient of BO -- TS weakly manages exploitation by gathering information about the true objective function after it obtains new observations. In this work, we improve the exploitation of TS by incorporating the $\varepsilon$-greedy policy, a well-established selection strategy in reinforcement learning. We first delineate two extremes of TS, namely the generic TS and the sample-average TS. The former promotes exploration, while the latter favors exploitation. We then adopt the $\varepsilon$-greedy policy to randomly switch between these two extremes. Small and large values of $\varepsilon$ govern exploitation and exploration, respectively. By minimizing two benchmark functions and solving an inverse problem of a steel cantilever beam, we empirically show that $\varepsilon$-greedy TS equipped with an appropriate $\varepsilon$ is more robust than its two extremes, matching or outperforming the better of the generic TS and the sample-average TS. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_00540 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Epsilon-Greedy Thompson Sampling to Bayesian Optimization Do, Bach Adebiyi, Taiwo Zhang, Ruda Machine Learning Optimization and Control Bayesian optimization (BO) has become a powerful tool for solving simulation-based engineering optimization problems thanks to its ability to integrate physical and mathematical understandings, consider uncertainty, and address the exploitation-exploration dilemma. Thompson sampling (TS) is a preferred solution for BO to handle the exploitation-exploration trade-off. While it prioritizes exploration by generating and minimizing random sample paths from probabilistic models -- a fundamental ingredient of BO -- TS weakly manages exploitation by gathering information about the true objective function after it obtains new observations. In this work, we improve the exploitation of TS by incorporating the $\varepsilon$-greedy policy, a well-established selection strategy in reinforcement learning. We first delineate two extremes of TS, namely the generic TS and the sample-average TS. The former promotes exploration, while the latter favors exploitation. We then adopt the $\varepsilon$-greedy policy to randomly switch between these two extremes. Small and large values of $\varepsilon$ govern exploitation and exploration, respectively. By minimizing two benchmark functions and solving an inverse problem of a steel cantilever beam, we empirically show that $\varepsilon$-greedy TS equipped with an appropriate $\varepsilon$ is more robust than its two extremes, matching or outperforming the better of the generic TS and the sample-average TS. |
| title | Epsilon-Greedy Thompson Sampling to Bayesian Optimization |
| topic | Machine Learning Optimization and Control |
| url | https://arxiv.org/abs/2403.00540 |