论文标题
结合进化和深度强化学习以进行政策搜索：一项调查
Combining Evolution and Deep Reinforcement Learning for Policy Search: a Survey
论文作者
论文摘要
在过去的几年中，深厚的神经进化和深厚的增强学习受到了很多关注。一些作品比较了它们，强调了他们的利弊，但是新兴趋势在于结合起来，从而从两全其美的世界中受益。在本文中，我们通过将文献组织成相关的作品组，并将每个组中的所有现有组合都组织成一个通用框架，从而对这种新兴趋势进行了调查。我们系统地涵盖了所有易于发表的论文，无论其出版状态如何，重点是组合机制，而不是实验结果。总的来说，我们总共涵盖了45种算法比2017年更多。我们希望这项工作将通过促进对方法之间关系的理解，从而有利于该领域的增长，从而导致更深入的分析，从而概述缺失有用的比较并提出新的机制组合。
Deep neuroevolution and deep Reinforcement Learning have received a lot of attention in the last years. Some works have compared them, highlighting theirs pros and cons, but an emerging trend consists in combining them so as to benefit from the best of both worlds. In this paper, we provide a survey of this emerging trend by organizing the literature into related groups of works and casting all the existing combinations in each group into a generic framework. We systematically cover all easily available papers irrespective of their publication status, focusing on the combination mechanisms rather than on the experimental results. In total, we cover 45 algorithms more recent than 2017. We hope this effort will favor the growth of the domain by facilitating the understanding of the relationships between the methods, leading to deeper analyses, outlining missing useful comparisons and suggesting new combinations of mechanisms.
