000			03635nam a22002657a 4500
003			OSt
005			20260520145012.0
008			260428b |||||||| |||| 00| 0 eng d
020			_a9781838644147
020			_a1838644148
041			_aeng
082			_a006.31 BIL-M
100			_aBilgin, Enes _979320
245			_aMastering reinforcement learning with Python _bbuild next-generation, self-learning models using reinforcement learning techniques and best practices
260			_aBirmingham, UK _bPackt Publishing _c2020
300			_axvi, 525p.
500			_aGet 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 Key Features: Understand how large-scale state-of-the-art RL algorithms and approaches work Apply RL to solve complex problems in marketing, robotics, supply chain, finance, cybersecurity, and more Explore tips and best practices from experts that will enable you to overcome real-world RL challenges
500			_aThis 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.
520			_aReinforcement learning (RL) is a field of artificial intelligence (AI) used to create self-learning, autonomous agents. Building on a strong theoretical foundation, this book takes a practical approach, using examples inspired by real-world industry problems to teach you state-of-the-art RL. Starting with bandit problems, Markov decision processes, and dynamic programming, the book provides an in-depth review of classical RL techniques, including 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 randomisation and curiosity-driven learning. As you advance, you'll explore many novel algorithms and their advanced implementations using modern Python libraries such as TensorFlow and Ray's RLlib. You'll also learn how to implement RL across robotics, supply chain management, marketing, finance, smart cities, and cybersecurity, while assessing trade-offs and avoiding common pitfalls. By the end of this book, you'll have mastered how to train and deploy your own RL agents for solving RL problems. What You Will Learn: Model and solve complex sequential decision-making problems using RL Develop a solid understanding of how state-of-the-art RL methods work Use Python and TensorFlow to code RL algorithms from scratch Parallelise and scale up your RL implementations using Ray's RLlib package Get in-depth knowledge of a wide variety of RL topics Understand the trade-offs between different RL approaches Discover and address the challenges of implementing RL in the real world
650			_aArtificial intelligence (AI) _979325
650			_aMachine learning (ML) _979326
650			_aReinforcement learning (RL) _979327
942			_cBK
999			_c199994 _d199994