Deep Reinforcement Learning Hands-On: A practical and easy-to-follow guide to RL from Q-learning and DQNs to PPO and RLHF
by Maxim Lapan
Explore deep reinforcement learning through a practical lens in this comprehensive guide. From basics like Q-learning and DQNs to advanced topics including PPO and RLHF, it provides easy-to-follow instructions and hands-on examples to help you implement RL solutions confidently and efficiently.
About This Book
This book offers a practical and easy-to-follow introduction to deep reinforcement learning, starting with foundational Q-learning and deep Q-networks (DQNs).
Readers will progress to more sophisticated methods like proximal policy optimization (PPO) and reinforcement learning from human feedback (RLHF), with step-by-step guidance for implementation.
Designed for hands-on learning, it emphasizes real-world application through code examples and exercises, making RL accessible without requiring advanced theoretical knowledge.
Whether you're a beginner or experienced developer, this guide equips you with the tools to build and train RL agents effectively.
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