Deep Reinforcement Learning in Action

Download or Read eBook Deep Reinforcement Learning in Action PDF written by Alexander Zai and published by Manning. This book was released on 2020-04-28 with total page 381 pages. Available in PDF, EPUB and Kindle.
Deep Reinforcement Learning in Action
Author :
Publisher : Manning
Total Pages : 381
Release :
ISBN-10 : 9781617295430
ISBN-13 : 1617295434
Rating : 4/5 (30 Downloads)

Book Synopsis Deep Reinforcement Learning in Action by : Alexander Zai

Book excerpt: Summary Humans learn best from feedback—we are encouraged to take actions that lead to positive results while deterred by decisions with negative consequences. This reinforcement process can be applied to computer programs allowing them to solve more complex problems that classical programming cannot. Deep Reinforcement Learning in Action teaches you the fundamental concepts and terminology of deep reinforcement learning, along with the practical skills and techniques you’ll need to implement it into your own projects. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the technology Deep reinforcement learning AI systems rapidly adapt to new environments, a vast improvement over standard neural networks. A DRL agent learns like people do, taking in raw data such as sensor input and refining its responses and predictions through trial and error. About the book Deep Reinforcement Learning in Action teaches you how to program AI agents that adapt and improve based on direct feedback from their environment. In this example-rich tutorial, you’ll master foundational and advanced DRL techniques by taking on interesting challenges like navigating a maze and playing video games. Along the way, you’ll work with core algorithms, including deep Q-networks and policy gradients, along with industry-standard tools like PyTorch and OpenAI Gym. What's inside Building and training DRL networks The most popular DRL algorithms for learning and problem solving Evolutionary algorithms for curiosity and multi-agent learning All examples available as Jupyter Notebooks About the reader For readers with intermediate skills in Python and deep learning. About the author Alexander Zai is a machine learning engineer at Amazon AI. Brandon Brown is a machine learning and data analysis blogger. Table of Contents PART 1 - FOUNDATIONS 1. What is reinforcement learning? 2. Modeling reinforcement learning problems: Markov decision processes 3. Predicting the best states and actions: Deep Q-networks 4. Learning to pick the best policy: Policy gradient methods 5. Tackling more complex problems with actor-critic methods PART 2 - ABOVE AND BEYOND 6. Alternative optimization methods: Evolutionary algorithms 7. Distributional DQN: Getting the full story 8.Curiosity-driven exploration 9. Multi-agent reinforcement learning 10. Interpretable reinforcement learning: Attention and relational models 11. In conclusion: A review and roadmap


Deep Reinforcement Learning in Action Related Books

Deep Reinforcement Learning in Action
Language: en
Pages: 381
Authors: Alexander Zai
Categories: Computers
Type: BOOK - Published: 2020-04-28 - Publisher: Manning

DOWNLOAD EBOOK

Summary Humans learn best from feedback—we are encouraged to take actions that lead to positive results while deterred by decisions with negative consequences
Foundations of Deep Reinforcement Learning
Language: en
Pages: 629
Authors: Laura Graesser
Categories: Computers
Type: BOOK - Published: 2019-11-20 - Publisher: Addison-Wesley Professional

DOWNLOAD EBOOK

The Contemporary Introduction to Deep Reinforcement Learning that Combines Theory and Practice Deep reinforcement learning (deep RL) combines deep learning and
Grokking Deep Reinforcement Learning
Language: en
Pages: 470
Authors: Miguel Morales
Categories: Computers
Type: BOOK - Published: 2020-11-10 - Publisher: Manning

DOWNLOAD EBOOK

Grokking Deep Reinforcement Learning uses engaging exercises to teach you how to build deep learning systems. This book combines annotated Python code with intu
Deep Reinforcement Learning Hands-On
Language: en
Pages: 717
Authors: Maxim Lapan
Categories: Computers
Type: BOOK - Published: 2024-11-12 - Publisher: Packt Publishing Ltd

DOWNLOAD EBOOK

Maxim Lapan delivers intuitive explanations and insights into complex reinforcement learning (RL) concepts, starting from the basics of RL on simple environment
Reinforcement Learning
Language: en
Pages: 517
Authors: Phil Winder Ph.D.
Categories: Computers
Type: BOOK - Published: 2020-11-06 - Publisher: "O'Reilly Media, Inc."

DOWNLOAD EBOOK

Reinforcement learning (RL) will deliver one of the biggest breakthroughs in AI over the next decade, enabling algorithms to learn from their environment to ach