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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.
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
A thorough introduction to reinforcement learning. Fun to read and highly relevant.
An essential read for anyone who wants to master deep reinforcement learning.
If you ever wondered what the theory is behind AI/ML and reinforcement learning, and how you can apply the techniques in your own projects, then this book is for you.
I highly recommend this book to anyone who aspires to master the fundamentals of DRL and seeks to follow a research or development career in this exciting field.
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