Hands-On Q-Learning with Python Practical Q-learning with OpenAI Gym, Keras, ...

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Hands-On Q-Learning with Python | Nazia Habib | 2019 | Packt publishing |

Q-learning is the reinforcement learning approach behind Deep-Q-Learning and is a values-based learning algorithm in RL. This book will help you get comfortable with developing the effective agents for Q learning and also make you learn to effectively develop and deploy Deep Q networks for complex AI applications.
A few chapters into the book, you will gain insights into model-free Q-learning and use deep Q-networks and double deep Q-networks to solve complex problems. This book will guide you in exploring use cases such as self-driving vehicles and OpenAI Gym's CartPole problem. You will also learn how to tune and optimize Q-networks and their hyperparameters. As you progress, you will understand the reinforcement learning approach to solving real-world problems. You will also explore how to use Q-learning and related algorithms in real-world applications such as scientific research. Toward the end, you'll gain a sense of what's in store for reinforcement learning.



Contents of Download:
9781789345803.epub (9.64 MB)
9781789345803.mobi (17.59 MB)


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