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%20on%20inputs%20outside%20the%20offline%20dataset.%20Prior%20approaches%20addressing%20this%20challenge%20have%20primarily%20focused%20on%20learning%20robust%20surrogate%20models.%20However%2C%20their%20search%20strategies%20are%20derived%20from%20the%20surrogate%20model%20rather%20than%20the%20actual%20offline%20data.%20To%20fill%20this%20important%20gap%2C%20we%20introduce%20a%20new%20learning-to-search%20perspective%20for%20offline%20optimization%20by%20reformulating%20it%20as%20an%20offline%20reinforcement%20learning%20problem.%20Our%20proposed%20policy-guided%20gradient%20search%20approach%20explicitly%20learns%20the%20best%20policy%20for%20a%20given%20surrogate%20model%20created%20from%20the%20offline%20data.%20Our%20empirical%20results%20on%20multiple%20benchmarks%20demonstrate%20that%20the%20learned%20optimization%20policy%20can%20be%20combined%20with%20existing%20offline%20surrogates%20to%20significantly%20improve%20the%20optimization%20performance.)
![Classification of optimization algorithms [7] | Download Scientific Diagram](https://www.researchgate.net/profile/Osman_Oezkaraca/publication/326067198/figure/download/fig2/AS:654153596559367@1532973779668/Classification-of-optimization-algorithms-7.png)
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![1: Classification of main optimization methods [30]. | Download ...](https://www.researchgate.net/publication/37434757/figure/fig9/AS:668942116864016@1536499637796/Classification-of-main-optimization-methods-30.png)
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