Key Features
- Learn with concise explanations, modern libraries, and diverse applications from games to stock trading and web navigation
- Develop deep RL models, improve their stability, and efficiently solve complex environments
- New content on RL from human feedback (RLHF), MuZero, and transformers
Who this book is for
This book is ideal for machine learning engineers, software engineers, and data scientists looking to learn and apply deep reinforcement learning in practice. It assumes familiarity with Python, calculus, and machine learning concepts. With practical examples and high-level overviews, it’s also suitable for experienced professionals looking to deepen their understanding of advanced deep RL methods and apply them across industries, such as gaming and finance