提出量子强化学习新评估方法，质疑部分性能优越性声称。
Benchmarking Quantum Reinforcement Learning
- 基于样本复杂度统计估计器构建新评测框架
- 实验显示量子强化学习优势并不显著，结果更复杂
- 适合关注量子优势实证研究的学者参考
强化学习（RL）的基准测试与统计验证仍无统一标准。量子计算兴起后，量子强化学习（QRL）的基准挑战更加复杂。为实现有效性能对比并推动该领域研究，我们提出一种新型基准方法：基于样本复杂度的统计估计器及统计优胜定义。针对QRL，该方法对部分先前宣称的性能优势提出质疑。我们在一个可调节复杂度的新基准环境中开展实验，虽仍发现潜在优势，但整体结果更为复杂。我们讨论了这些结果的局限性，并探讨其对量子优势实证研究的启示。
原文摘要 · Abstract (English)
Benchmarking and establishing proper statistical validation metrics for reinforcement learning (RL) remain ongoing challenges, where no consensus has been established yet. The emergence of quantum computing and its potential applications in quantum reinforcement learning (QRL) further complicate benchmarking efforts. To enable valid performance comparisons and to streamline current research in this area, we propose a novel benchmarking methodology, which is based on a statistical estimator for sample complexity and a definition of statistical outperformance. Furthermore, considering QRL, our methodology casts doubt on some previous claims regarding its superiority. We conducted experiments on a novel benchmarking environment with flexible levels of complexity. While we still identify possible advantages, our findings are more nuanced overall. We discuss the potential limitations of these results and explore their implications for empirical research on quantum advantage in QRL.
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