{"product_id":"mastering-reinforcement-learning-with-python-build-next-generation-self-learning-models-using-reinforcement-learning-techniques-and-best-practices-paperback","title":"Mastering Reinforcement Learning with Python: Build next-generation, self-learning models using reinforcement learning techniques and best practices - Paperback","description":"\u003cp\u003eby \u003cb\u003eEnes Bilgin\u003c\/b\u003e (Author)\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003cstrong\u003eGet hands-on experience in creating state-of-the-art reinforcement learning agents using TensorFlow and RLlib to solve complex real-world business and industry problems with the help of expert tips and best practices\u003c\/strong\u003e\u003c\/p\u003e\u003cp\u003e\u003cbr\u003e\u003c\/p\u003e\u003cp\u003e\u003cstrong\u003eKey Features: \u003c\/strong\u003e\u003c\/p\u003e\u003cul\u003e\n\u003cli\u003eUnderstand how large-scale state-of-the-art RL algorithms and approaches work\u003c\/li\u003e\n\u003cli\u003eApply RL to solve complex problems in marketing, robotics, supply chain, finance, cybersecurity, and more\u003c\/li\u003e\n\u003cli\u003eExplore tips and best practices from experts that will enable you to overcome real-world RL challenges\u003c\/li\u003e\n\u003c\/ul\u003e\u003cp\u003e\u003cbr\u003e\u003c\/p\u003e\u003cp\u003e\u003cstrong\u003eBook Description: \u003c\/strong\u003e\u003c\/p\u003e\u003cp\u003eReinforcement learning (RL) is a field of artificial intelligence (AI) used for creating self-learning autonomous agents. Building on a strong theoretical foundation, this book takes a practical approach and uses examples inspired by real-world industry problems to teach you about state-of-the-art RL.\u003c\/p\u003e\u003cp\u003e\u003cbr\u003e\u003c\/p\u003e\u003cp\u003eStarting with bandit problems, Markov decision processes, and dynamic programming, the book provides an in-depth review of the classical RL techniques, such as Monte Carlo methods and temporal-difference learning. After that, you will learn about deep Q-learning, policy gradient algorithms, actor-critic methods, model-based methods, and multi-agent reinforcement learning. Then, you'll be introduced to some of the key approaches behind the most successful RL implementations, such as domain randomization and curiosity-driven learning.\u003c\/p\u003e\u003cp\u003e\u003cbr\u003e\u003c\/p\u003e\u003cp\u003eAs you advance, you'll explore many novel algorithms with advanced implementations using modern Python libraries such as TensorFlow and Ray's RLlib package. You'll also find out how to implement RL in areas such as robotics, supply chain management, marketing, finance, smart cities, and cybersecurity while assessing the trade-offs between different approaches and avoiding common pitfalls.\u003c\/p\u003e\u003cp\u003e\u003cbr\u003e\u003c\/p\u003e\u003cp\u003eBy the end of this book, you'll have mastered how to train and deploy your own RL agents for solving RL problems.\u003c\/p\u003e\u003cp\u003e\u003cbr\u003e\u003c\/p\u003e\u003cp\u003e\u003cstrong\u003eWhat You Will Learn: \u003c\/strong\u003e\u003c\/p\u003e\u003cul\u003e\n\u003cli\u003eModel and solve complex sequential decision-making problems using RL\u003c\/li\u003e\n\u003cli\u003eDevelop a solid understanding of how state-of-the-art RL methods work\u003c\/li\u003e\n\u003cli\u003eUse Python and TensorFlow to code RL algorithms from scratch\u003c\/li\u003e\n\u003cli\u003eParallelize and scale up your RL implementations using Ray's RLlib package\u003c\/li\u003e\n\u003cli\u003eGet in-depth knowledge of a wide variety of RL topics\u003c\/li\u003e\n\u003cli\u003eUnderstand the trade-offs between different RL approaches\u003c\/li\u003e\n\u003cli\u003eDiscover and address the challenges of implementing RL in the real world\u003c\/li\u003e\n\u003c\/ul\u003e\u003cp\u003e\u003cbr\u003e\u003c\/p\u003e\u003cp\u003e\u003cstrong\u003eWho This Book Is For: \u003c\/strong\u003e\u003c\/p\u003e\u003cp\u003eThis book is for expert machine learning practitioners and researchers looking to focus on hands-on reinforcement learning with Python by implementing advanced deep reinforcement learning concepts in real-world projects. Reinforcement learning experts who want to advance their knowledge to tackle large-scale and complex sequential decision-making problems will also find this book useful. Working knowledge of Python programming and deep learning along with prior experience in reinforcement learning is required.\u003c\/p\u003e\u003cdiv\u003e\n\u003cstrong\u003eNumber of Pages:\u003c\/strong\u003e 544\u003c\/div\u003e\u003cdiv\u003e\n\u003cstrong\u003eDimensions:\u003c\/strong\u003e 1.1 x 9.25 x 7.5 IN\u003c\/div\u003e\u003cdiv\u003e\n\u003cstrong\u003ePublication Date:\u003c\/strong\u003e December 18, 2020\u003c\/div\u003e","brand":"Books by splitShops","offers":[{"title":"Default Title","offer_id":47412434731186,"sku":"9781838644147","price":73.42,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0770\/3891\/1666\/files\/61624ae7ca589f9fbe7ee580ab8db60e.webp?v=1778321262","url":"https:\/\/box.dadyminds.org\/products\/mastering-reinforcement-learning-with-python-build-next-generation-self-learning-models-using-reinforcement-learning-techniques-and-best-practices-paperback","provider":"DADYMINDS BOX","version":"1.0","type":"link"}