Mastering reinforcement learning with python : build next-generation, self-learning models using reinforcement learning techniques and best practices / Enes Bilgin

Includes bibliographical references and index.

Introduction to Reinforcement Learning -- Multi-armed Bandits -- Contextual Bandits -- Makings of the Markov Decision Process -- Solving the Reinforcement Learning Problem -- Deep Q-Learning at Scale -- Policy Based Methods -- Model-Based Methods -- Multi-Agent Reinforcement Learning -- Machine Teaching -- Generalization and Domain Randomization -- Meta-reinforcement learning -- Other Advanced Topics -- Autonomous Systems -- Supply Chain Management -- Marketing, Personalization and Finance -- Smart City and Cybersecurity -- Challenges and Future Directions in Reinforcement Learning.

Get 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 Book Description Reinforcement 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.