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We describe the foundations, the algorithms and the applications of deep reinforcement learning. We assume an undergraduate-level of understanding of computer science and artificial intelligence the programming language of this book is Python. 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. The aim of this book is to provide a comprehensive overview of the field of deep reinforcement learning. The successes in research have not gone unnoticed by educators, and universities have started to offer courses on the subject. The computer seems to truly possess aspects of human learning this goes to the heart of the dream of artificial intelligence.

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. In some applications they have even become better than the best humans, such as in Atari, Go, poker and StarCraft. They have learned to fly model helicopters and perform aerobatic manoeuvers such as loops and rolls.

In all these fields, computer programs have taught themselves to solve difficult problems. Impressive results were achieved in activities as diverse as autonomous driving, game playing, molecular recombination, and robotics. In an empirical ablation on BRExIt's algorithmic variants in the game Connect4 against a set of fixed test agents, we provide statistical evidence that BRExIt learns well-performing policies with greater sample efficiency than ExIt.ĭeep reinforcement learning has gathered much attention recently. BRExIt aims to (1) improve feature shaping in the apprentice, with a policy head predicting opponent policies as an auxiliary task, and (2) bias opponent moves in planning towards the given or learnt opponent model, to generate apprentice targets that better approximate a best response. We propose Best Response Expert Iteration (BRExIt), which accelerates learning in games by incorporating opponent models into the state-of-the-art learning algorithm Expert Iteration (ExIt). Finding a best response policy is a central objective in game theory and multi-agent learning, with modern population-based training approaches employing reinforcement learning algorithms as best-response oracles to improve play against candidate opponents (typically previously learnt policies).

Catan universe promotion code september 2017