Deep Reinforcement Learning Hands-On - Third Edition

Deep Reinforcement Learning Hands-On covers essential RL techniques, from foundational concepts like Q-learning to advanced methods such as PPO and RLHF. With this hands-on approach, you will learn to apply cutting-edge reinforcement learning methods using PyTorch and modern RL libraries, and gain practical knowledge applicable across industries.

What this Book will help me do

• Master reinforcement learning fundamentals, including Q-learning and deep Q-networks.
• Develop and train RL models using advanced libraries like PyTorch and Gymnasium.
• Implement and evaluate state-of-the-art RL algorithms such as TRPO, PPO, and MuZero.
• Apply reinforcement learning to real-world scenarios, from gaming to stock trading.
• Understand RL techniques with fine-tuned engineering approaches for performance optimization.

Author(s)

Maxim Lapan is an expert in machine learning and artificial intelligence, specializing in reinforcement learning techniques. With years of experience in the field, Maxim provides a hands-on and practical approach to learning RL, focusing on both foundational understanding and practical applications. His expertise helps readers grasp complex concepts effectively through code and examples.

Who is it for?

This book is designed for machine learning practitioners, including software engineers and data scientists with a basic understanding of Python, calculus, and machine learning. It is ideal for those aiming to use reinforcement learning to solve practical problems in industries like gaming, finance, and web technologies. It suits both learners new to RL and seasoned experts looking to deepen their expertise.