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Deep Reinforcement Learning in Action
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 Markov decision processes
3. Predicting the best states and Deep Q-networks
4. Learning to pick the best Policy gradient methods
5. Tackling more complex problems with actor-critic methods
PART 2 - ABOVE AND BEYOND
6. Alternative optimization Evolutionary algorithms
7. Distributional Getting the full story
8.Curiosity-driven exploration
9. Multi-agent reinforcement learning
10. Interpretable reinforcement Attention and relational models
11. In A review and roadmap
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 Markov decision processes
3. Predicting the best states and Deep Q-networks
4. Learning to pick the best Policy gradient methods
5. Tackling more complex problems with actor-critic methods
PART 2 - ABOVE AND BEYOND
6. Alternative optimization Evolutionary algorithms
7. Distributional Getting the full story
8.Curiosity-driven exploration
9. Multi-agent reinforcement learning
10. Interpretable reinforcement Attention and relational models
11. In A review and roadmap
384 pages, Paperback
Published March 31, 2020
7 people are currently reading
71 people want to read
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Displaying 1 - 2 of 2 reviews
October 3, 2022
If you are newbie in this field, I strongly recommend to read this book. The only drawback is, in my opinion, a bit chaotic examples, which could be written slitghtly more minimalistic, especially if you try to understand the core idea.
Luckily, the code is freely available on internet!
More of such books
Luckily, the code is freely available on internet!
More of such books
June 11, 2020
Good hands on introduction book,read this after watching david silver lectures and reading sutton and this book explained its implementation well
Displaying 1 - 2 of 2 reviews