论文标题
一项关于基于深入学习的深入学习方法的调查，以适应和泛化
A Survey on Deep Reinforcement Learning-based Approaches for Adaptation and Generalization
论文作者
论文摘要
深度强化学习（DRL）旨在创建聪明的代理商，可以在现实世界中学习有效地解决复杂问题。通常，两个学习目标：适应和概括用于基础DRL算法在不同的任务和域上的性能。本文介绍了一项有关基于DRL的适应和泛化方法的最新发展的调查。我们首先在任务和领域的背景下制定这些目标。然后，我们回顾了这些方法下的最新作品，并讨论了未来的研究方向，可以增强DRL算法的适应性和可推广性，并可能使其适用于广泛的现实世界中的问题。
Deep Reinforcement Learning (DRL) aims to create intelligent agents that can learn to solve complex problems efficiently in a real-world environment. Typically, two learning goals: adaptation and generalization are used for baselining DRL algorithm's performance on different tasks and domains. This paper presents a survey on the recent developments in DRL-based approaches for adaptation and generalization. We begin by formulating these goals in the context of task and domain. Then we review the recent works under those approaches and discuss future research directions through which DRL algorithms' adaptability and generalizability can be enhanced and potentially make them applicable to a broad range of real-world problems.
