Mastering Reinforcement Learning with Python
This book focuses on expert-level explanations and implementations of scalable reinforcement learning algorithms and approaches. Starting with the fundamentals, the book covers state-of-the-art methods from bandit problems to meta-reinforcement learning. You’ll also explore practical examples inspired by real-life problems from the industry.
Offered by
Difficulty Level
Intermediate
Completion Time
18h8m
Language
English
About Book
Who Is This Book For?
This 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.
Mastering Reinforcement Learning with Python
- About Book
- Who Is This Book For?
- Book Content
Book content
chapters • 18h8m total length
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
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