元强化学习综述

强化学习在游戏对弈、系统控制等领域内表现出良好的性能，如何使用少量样本快速学习新任务是强化学习中亟需解决的问题。目前的有效解决方法是将元学习应用在强化学习中，由此所产生的元强化学习日益成为强化学习领域中的研究热点。为了帮助后续研究人员快速并全面了解元强化学习领域，根据近年来的元强化学习文献对研究方法进行梳理，将其归纳成基于循环网络的元强化学习、基于上下文的元强化学习、基于梯度的元强化学习、基于分层的元强化学习和离线元强化学习，对五种类型的研究方法进行对比分析，简要阐述了元强化学习的基本理论和面临的挑战，最后基于当前研究现状讨论了元强化学习的未来发展前景。

Survey on meta reinforcement learning

Although reinforcement learning shows good performance in game playing, system control and other fields, how to use a small number of samples to learn new tasks quickly is an urgent problem to be solved in reinforcement learning. At present, applying meta learning to reinforcement learning has been one of the most effective solutions, and the generated meta reinforcement learning has increasingly become a research hotspot in the field of reinforcement learning. To help researchers understand the field of meta reinforcement learning quickly, this paper sorted out the algorithms according to the literatures of meta reinforcement learning in recent years, summarized them into CNN-based meta-RL, context-based meta-RL, gradient-based meta-RL, hierarchical-based meta-RL and offline meta-RL and compared five types of algorithms. In addition, it briefly described the basic theories and challenges of meta reinforcement learning. Finally, this paper also discussed the future development of meta reinforcement learning based on the current research status.