论文标题
对强化学习的基准测试框架的调查
A survey of benchmarking frameworks for reinforcement learning
论文作者
论文摘要
强化学习最近经历了机器学习社区的突出性。通过不断开发新技术解决强化学习问题有许多方法。在使用强化学习解决问题时，需要克服各种困难的挑战。为了确保该领域的进展，基准对于测试新算法并与其他方法进行比较非常重要。因此，进行公平比较的结果的可重复性对于确保对改进进行准确的判断至关重要。本文概述了对强化学习基准测试的不同贡献，并讨论了如何帮助研究人员应对强化学习面临的挑战。讨论的贡献是文献中最常用和最新的。本文在实施，任务方面讨论了贡献，并提供了具有基准测试的算法实现。该调查旨在引起人们对可用的广泛强化学习基准任务的关注，并鼓励以标准化的方式进行研究。此外，这项调查是不熟悉可用于开发和测试新的强化学习算法的不同任务的研究人员的概述。
Reinforcement learning has recently experienced increased prominence in the machine learning community. There are many approaches to solving reinforcement learning problems with new techniques developed constantly. When solving problems using reinforcement learning, there are various difficult challenges to overcome. To ensure progress in the field, benchmarks are important for testing new algorithms and comparing with other approaches. The reproducibility of results for fair comparison is therefore vital in ensuring that improvements are accurately judged. This paper provides an overview of different contributions to reinforcement learning benchmarking and discusses how they can assist researchers to address the challenges facing reinforcement learning. The contributions discussed are the most used and recent in the literature. The paper discusses the contributions in terms of implementation, tasks and provided algorithm implementations with benchmarks. The survey aims to bring attention to the wide range of reinforcement learning benchmarking tasks available and to encourage research to take place in a standardised manner. Additionally, this survey acts as an overview for researchers not familiar with the different tasks that can be used to develop and test new reinforcement learning algorithms.
