Practical deep reinforcement learning with python / Ivan Gridin

Includes index.

1. Introducing Reinforcement Learning -- 2. Playing Monopoly and Markov Decision Process -- 3. Training in Gym -- 4. Struggling with Multi- Armed Bandits -- 5. Blackjack in Monte Carlo -- 6. Escaping Maze with Q-Learning -- 7. Discretization -- Part II. Deep Reinforcement Learning -- 8. TensorFlow, PyTorch, and Your First Neural Network -- 9. Deep Q-Network and Lunar Lander -- 10. Defending Atlantis With Double Deep Q-Network -- 11. From Q-Learning to Policy-Gradient -- 12. Stock Trading With Actor-Critic -- 13. What Is Next?.

This book introduces readers to reinforcement learning from a pragmatic point of view. The book does involve mathematics, but it does not attempt to overburden the reader, who is a beginner in the field of reinforcement learning. The book brings a lot of innovative methods to the reader's attention in much practical learning, including Monte-Carlo, Deep Q-Learning, Policy Gradient, and Actor-Critical methods. While you understand these techniques in detail, the book also provides a real implementation of these methods and techniques using the power of TensorFlow and PyTorch. The book covers some enticing projects that show the power of reinforcement learning, and not to mention that everything is concise, up-to-date, and visually explained.