论文标题
纤维：用于加强学习和基于人群方法的有效开发和分布式培训的平台
Fiber: A Platform for Efficient Development and Distributed Training for Reinforcement Learning and Population-Based Methods
论文作者
论文摘要
通过增加计算量，机器学习的最新进展始终可以实现。强化学习（RL）和基于人群的方法尤其对基础分布式计算框架提出了独特的挑战，并为效率和灵活性带来了独特的挑战。这些挑战包括与模拟的频繁交互，对动态缩放的需求以及对不同后端的采用成本和一致性低的用户界面的需求。在本文中，我们通过引入Fiber（用于RL和基于人群的方法的可扩展的分布式计算框架，仍在保留研究和实际应用的发展效率和灵活性，以解决这些挑战。纤维旨在将大规模平行计算的可访问性显着扩大到本来复杂的RL和基于人群的方法的用户，而无需专门的计算专业知识。
Recent advances in machine learning are consistently enabled by increasing amounts of computation. Reinforcement learning (RL) and population-based methods in particular pose unique challenges for efficiency and flexibility to the underlying distributed computing frameworks. These challenges include frequent interaction with simulations, the need for dynamic scaling, and the need for a user interface with low adoption cost and consistency across different backends. In this paper we address these challenges while still retaining development efficiency and flexibility for both research and practical applications by introducing Fiber, a scalable distributed computing framework for RL and population-based methods. Fiber aims to significantly expand the accessibility of large-scale parallel computation to users of otherwise complicated RL and population-based approaches without the need to for specialized computational expertise.
