  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.
