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Product Details 基本信息 ISBN-13 书号 9781484268087 Author 作者 Sanghi Format 版本 平装-胶订 Pages Number 页数 382页 Publisher 出版社 Apress Publication Date 出版日期 2021-04-14 Language 语种 其它（含多语） Book Contents 内容简介 Deep reinforcement learning is a fast-growing discipline that is making a significant impact in fields of autonomous vehicles, robotics, healthcare, finance, and many more. This book covers deep reinforcement learning using deep-q learning and policy gradient models with coding exercise.You’ll begin by reviewing the Markov decision processes, Bellman equations, and dynamic programming that form the core concepts and foundation of deep reinforcement learning. Next, you’ll study model-free learning followed by function approximation using neural networks and deep learning. This is followed by various deep reinforcement learning algorithms such as deep q-networks, various
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Product Details 基本信息 ISBN-13 书号 9781839210686 Author 作者 Ravichandiran Format 版本 平装-胶订 Pages Number 页数 760页 Publisher 出版社 Packt Publishing Publication Date 出版日期 2020-09-30 Product Dimensions 商品尺寸 23.5 x 19.1 x 3.8 cm Shipping Weight 商品重量 1.3 kg Language 语种 其它（含多语） Book Contents 内容简介 With significant enhancements in the quality and quantity of algorithms in recent years, this second edition of Hands-On Reinforcement Learning with Python has been revamped into an example-rich guide to learning state-of-the-art reinforcement learning (RL) and deep RL algorithms with TensorFlow 2 and the OpenAI Gym toolkit.In addition to exploring RL basics and foundational concepts such as Bellman equation, Markov decision processes, and dynamic programming algorithms, this second edition dives deep into the full spectrum of value-based, policy-based, and
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