Qiang Yang @ HKUST
Lixin Duan @ UESTC
Mingsheng Long @ THU
Judy Hoffman @ UC Berkeley & Stanford
Fuzhen Zhuang @ ICT, CAS
Fei Sha @ USC
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最近更新:2019-12-09
报告摘要:迁移学习一直是机器学习领域的难点问题,其目标是在数据分布变化的条件下实现强泛化能力。经过长期探索,逐步缩小了迁移学习的泛化理论与学习算法之间的鸿沟,获得了更紧致的泛化界和更优的学习器。此次报告将按照发展历程介绍迁移学习的代表性泛化理论及学习算法,重点介绍我们的间隔泛化理论及其对抗学习算法,同时介绍我们关于迁移学习的最新进展,包括局部泛化理论、迁移推理中的概率校准和无监督迁移学习算法。
Reinforcement Learning (RL) is a key technique to address sequential decision-making problems and is crucial to realize advanced artificial intelligence. Recent years have witnessed remarkable progress in RL by virtue of the fast development of deep neural networks. Along with the promising prospects of RL in numerous domains, such as robotics and game-playing, transfer learning has arisen as an important technique to tackle various challenges faced by RL, by transferring knowledge from external expertise to accelerate 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 RL backbones, and practical applications. We also draw connections between transfer learning and other relevant topics from the RL perspective and explore their potential challenges as well as open questions that await future research progress.