论文标题
深入强化学习中的转移学习：一项调查
Transfer Learning in Deep Reinforcement Learning: A Survey
论文作者
论文摘要
强化学习是解决顺序决策问题的学习范式。近年来，对深度神经网络的快速发展，在加强学习方面取得了显着进步。除了在诸如机器人技术和游戏之类的众多领域中进行强化学习的前景，转移学习还出现了，以应对强化学习所面临的各种挑战，通过将知识从外部专业知识转移以促进学习过程的效率和有效性。在这项调查中，我们系统地研究了在深度强化学习的背景下转移学习方法的最新进展。具体而言，我们提供了一个框架，用于对最新的转移学习方法进行分类，根据该方法，我们分析了他们的目标，方法，兼容的增强型学习骨架和实际应用。从强化学习的角度来看，我们还从转移学习与其他相关主题之间建立了联系，并探索了他们等待未来研究进展的潜在挑战。
Reinforcement learning is a learning paradigm for solving sequential decision-making problems. Recent years have witnessed remarkable progress in reinforcement learning upon the fast development of deep neural networks. Along with the promising prospects of reinforcement learning in numerous domains such as robotics and game-playing, transfer learning has arisen to tackle various challenges faced by reinforcement learning, by transferring knowledge from external expertise to facilitate the efficiency and effectiveness of the learning process. In this survey, we systematically investigate the recent progress of transfer learning approaches in the context of deep reinforcement learning. Specifically, we provide a framework for categorizing the state-of-the-art transfer learning approaches, under which we analyze their goals, methodologies, compatible reinforcement learning backbones, and practical applications. We also draw connections between transfer learning and other relevant topics from the reinforcement learning perspective and explore their potential challenges that await future research progress.
