تقرير
Position: Benchmarking is Limited in Reinforcement Learning Research
| العنوان: | Position: Benchmarking is Limited in Reinforcement Learning Research |
|---|---|
| المؤلفون: | Jordan, Scott M., White, Adam, da Silva, Bruno Castro, White, Martha, Thomas, Philip S. |
| سنة النشر: | 2024 |
| المجموعة: | Computer Science Statistics |
| مصطلحات موضوعية: | Computer Science - Machine Learning, Statistics - Methodology |
| الوصف: | Novel reinforcement learning algorithms, or improvements on existing ones, are commonly justified by evaluating their performance on benchmark environments and are compared to an ever-changing set of standard algorithms. However, despite numerous calls for improvements, experimental practices continue to produce misleading or unsupported claims. One reason for the ongoing substandard practices is that conducting rigorous benchmarking experiments requires substantial computational time. This work investigates the sources of increased computation costs in rigorous experiment designs. We show that conducting rigorous performance benchmarks will likely have computational costs that are often prohibitive. As a result, we argue for using an additional experimentation paradigm to overcome the limitations of benchmarking. Comment: 19 pages, 13 figures, The Forty-first International Conference on Machine Learning (ICML 2024) |
| نوع الوثيقة: | Working Paper |
| URL الوصول: | http://arxiv.org/abs/2406.16241 |
| رقم الأكسشن: | edsarx.2406.16241 |
| قاعدة البيانات: | arXiv |
| الوصف غير متاح. |