Deep Reinforcement Learning Hands-On
A practical and easy-to-follow guide to RL from Q-learning and DQNs to PPO and RLHF
Created by Maxim Lapan
Explore the world of deep reinforcement learning by building real projects and mastering key algorithms. Move from foundational concepts to advanced techniques with practical examples using PyTorch and modern RL libraries. Gain hands-on experience that prepares you for real-world applications across diverse industries.
Packt | Nov 2024 | 716 min
What You Will Learn
You will start by implementing core RL algorithms in approachable projects, gradually moving to more complex environments and advanced methods. Each topic is explained with clarity and supported by hands-on coding exercises, allowing you to see how theory translates into practice. By the end, you will have built a solid foundation and practical skills.
Key Features
- Build and train deep RL models using PyTorch and popular RL libraries
- Apply advanced algorithms like DQN, PPO, and RLHF to real-world problems
- Understand and implement cutting-edge methods for stable and efficient learning
Target Audience
Designed for machine learning engineers, software developers, and data scientists who already know Python and basic ML concepts. If you want to deepen your understanding of deep RL and apply it to areas like gaming, finance, or optimization, this course will help you build both confidence and expertise.
