Newly developed interfaces for Python, Dask, and PySpark enable the use of Alchemist with additional data analysis frameworks. We also briefly discuss the combination of Alchemist with RLlib, an increasingly popular library for reinforcement learning, and consider the benefits of leveraging HPC simulations in reinforcement learning. Finally, since data transfer between the client applications and Alchemist are the main overhead Alchemist encounters, we give a qualitative assessment of these transfer times with respect to different~factors.

翻译：Python、Dask 和 PySpark 的新开发界面使得能用更多的数据分析框架来利用炼金师。 我们还简要地讨论了炼金师与RLlib(一个越来越受欢迎的强化学习图书馆)的结合,并审议了利用HPC模拟来强化学习的好处。 最后,由于客户应用程序和炼金师之间的数据传输是替代炼金师的主要间接遭遇,我们对这些不同的叶源物的转移时间进行了定性评估。