Deep Reinforcement Learning in Action
By 作者:Alexander Zai and Brandon Brown
pages 页数: 277 pages
Publisher Finelybook 出版社: Manning Publications; 1 edition (May 12, 2020)
Language 语言: English
The Book Description robot was collected from Amazon and arranged by Finelybook
Humans learn best from feedback—we are encouraged to take actions that lead to positive results while deterred By 作者: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.
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.
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 作者: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.
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
Deep Reinforcement Learning in Action 9781617295430.zip }