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Hands-On Q-Learning with Python [electronic resource]. 1st edition
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| 자료유형 | 단행본 |
|---|---|
| 서명/저자사항 | Hands-On Q-Learning with Python/ Habib, Nazia. [electronic resource]. |
| 개인저자 | Habib, Nazia,author. |
| 단체저자명 | Safari, an O'Reilly Media Company. |
| 판사항 | 1st edition. |
| 발행사항 | Packt Publishing, 2019. |
| 형태사항 | 1 online resource (212 pages). |
| ISBN | 9781789345759 1789345758 |
| 기타표준부호 | 9781789345803 |
| 요약 | Leverage the power of reward-based training for your deep learning models with Python Key Features Understand Q-learning algorithms to train neural networks using Markov Decision Process (MDP) Study practical deep reinforcement learning using Q-Networks Explore state-based unsupervised learning for machine learning models Book Description Q-learning is a machine learning algorithm used to solve optimization problems in artificial intelligence (AI). It is one of the most popular fields of study among AI researchers. This book starts off by introducing you to reinforcement learning and Q-learning, in addition to helping you become familiar with OpenAI Gym as well as libraries such as Keras and TensorFlow. 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 scientific research. Toward the end, you'll gain insight into what's in store for reinforcement learning. By the end of this book, you will be equipped with the skills you need to solve reinforcement learning problems using Q-learning algorithms with OpenAI Gym, Keras, and TensorFlow. What you will learn Explore the fundamentals of reinforcement learning and the state-action-reward process Understand Markov decision processes Get well-versed with libraries such as Keras, and TensorFlow Create and deploy model-free learning and deep Q-learning agents with TensorFlow, Keras, and OpenAI Gym Choose and optimize a Q-network's learning parameters and fine-tune its performance Discover real-world applications and use cases of Q-learning Who this book is for If you are a machine learning developer, engineer, or professional who wants to explore the deep learning approach for a complex environment, then this is the book for you. Proficiency in Python programming and basic understanding of decision-making in reinforcement learning is assumed. Downloading the example code for this ebook: You can download the example code files for this ebook on GitHub at the following link: https://github.com/PacktPublishing/Hands-On-Q-Learning-with-Pyt ... |
| 발행자주기 | Made available through: Safari, an O'Reilly Media Company. |
| 일반주제명 | Python. |
| 언어 | 영어 |
| 바로가기 |  |
