Deep reinforcement learning has gathered much attention recently. Impressive results were achieved in activities as diverse as autonomous driving, game playing, molecular recombination, and robotics. In all these fields, computer programs have taught themselves to solve difficult problems. They have learned to fly model helicopters and perform aerobatic manoeuvers such as loops and rolls. In some applications they have even become better than the best humans, such as in Atari, Go, poker and StarCraft. The way in which deep reinforcement learning explores complex environments reminds us of how children learn, by playfully trying out things, getting feedback, and trying again. The computer seems to truly possess aspects of human learning; this goes to the heart of the dream of artificial intelligence. The successes in research have not gone unnoticed by educators, and universities have started to offer courses on the subject. The aim of this book is to provide a comprehensive overview of the field of deep reinforcement learning. The book is written for graduate students of artificial intelligence, and for researchers and practitioners who wish to better understand deep reinforcement learning methods and their challenges. We assume an undergraduate-level of understanding of computer science and artificial intelligence; the programming language of this book is Python. We describe the foundations, the algorithms and the applications of deep reinforcement learning. We cover the established model-free and model-based methods that form the basis of the field. Developments go quickly, and we also cover advanced topics: deep multi-agent reinforcement learning, deep hierarchical reinforcement learning, and deep meta learning.
翻译:深层强化学习最近引起了许多关注。 在诸如自主驾驶、游戏游戏、分子重组和机器人等多样化活动中,取得了令人印象深刻的成果。 在所有这些领域,计算机程序都教导自己如何解决困难的问题。 他们学会了飞行模拟直升机和进行循环和滚动等有氧运动。 在有些应用中,他们甚至比最优秀的人更好,例如在阿塔里、戈、扑克和星空计算机。 深层强化学习探索复杂环境的方式提醒我们儿童如何通过玩耍、获取反馈和再次尝试来深层强化学习。 计算机似乎真正拥有人类学习的方方面面; 这涉及到人造智能的梦想的核心。 研究的成功并没有被教育者所忽视,大学也开始提供这方面的课程。 这本书的目的是全面概述深层强化学习领域。 这本书是为高级研究生编写的,为深层自由智能学习方法及其挑战的研究人员和从业者撰写的。 我们承担了对计算机科学和人造智能基础的本科水平的理解,我们学习了深层强化基础; 隐藏了这个语言的学习基础。 我们学习了这个基础的学习基础。 我们学习了这个语言的封面基础。