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: Deep Reinforcement Learning: Frontiers of Artificial Intelligence
: Mohit Sewak
: Springer
: 2019
: 215
:
: pdf (true)
: 15.9 MB

Reinforcement Learning has evolved a long way with the enhancements from deep learning. Recent research efforts into combining deep learning with Reinforcement Learning have led to the development of some very powerful deep Reinforcement Learning systems, algorithms, and agents which have already achieved some extraordinary accomplishment. Not only have such systems surpassed the capabilities of most of the classical and non-deep-learning-based Reinforcement Learning agents, but have also started outperforming the best of human intelligence at tasks which were believed to require extreme human intelligence, creativity, and planning skills. Some of the DQN-based agents consistently beating the best of human players at the complex game of AlphaGo are very good examples of this.

This book starts with the basics of Reinforcement Learning and explains each concept using very intuitive and easy to understand examples and applications. Continuing with similar examples, this book then builds upon to introduce some cutting-edge researches and advancements that make Reinforcement Learning outperform many of the other (artificial) intelligent systems. This book aims to not only equip the readers with just the mathematical understanding of multiple cutting-edge Reinforcement Learning algorithms, but also prepares them to implement these and similar advanced Deep Reinforcement Learning agents and system hands-on in their own domain and application area.

This book is intended for readers who want to both understand and apply advanced concepts in a field that combines the best of two worlds deep learning and reinforcement learning to tap the potential of advanced artificial intelligence for creating real-world applications and game-winning algorithms.

Deep Reinforcement Learning: Frontiers of Artificial Intelligence








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