- We will send in 10–14 business days.
/images/books/13449304/9783902613141.jpg)
- Publisher: IntechOpen
- Year: 2008
- Pages: 436
- ISBN-10: 3902613149
- ISBN-13: 9783902613141
- Format: 17 x 24.4 x 2.4 cm, hardcover
- Language: English
- SAVE -10% with code: EXTRA
Reinforcement Learning (e-book) (used book) | bookbook.eu
Reviews
Description
Brains rule the world, and brain-like computation is increasingly used in computers and electronic devices. Brain-like computation is about processing and interpreting data or directly putting forward and performing actions. Learning is a very important aspect. This book is on reinforcement learning which involves performing actions to achieve a goal. The first 11 chapters of this book describe and extend the scope of reinforcement learning. The remaining 11 chapters show that there is already wide usage in numerous fields. Reinforcement learning can tackle control tasks that are too complex for traditional, hand-designed, non-learning controllers. As learning computers can deal with technical complexities, the tasks of human operators remain to specify goals on increasingly higher levels. This book shows that reinforcement learning is a very dynamic area in terms of theory and applications and it shall stimulate and encourage new research in this field.
EXTRA 10 % discount with code: EXTRA
The promotion ends in 17d.14:15:54
The discount code is valid when purchasing from 10 €. Discounts do not stack.
- Publisher: IntechOpen
- Year: 2008
- Pages: 436
- ISBN-10: 3902613149
- ISBN-13: 9783902613141
- Format: 17 x 24.4 x 2.4 cm, hardcover
- Language: English English
Brains rule the world, and brain-like computation is increasingly used in computers and electronic devices. Brain-like computation is about processing and interpreting data or directly putting forward and performing actions. Learning is a very important aspect. This book is on reinforcement learning which involves performing actions to achieve a goal. The first 11 chapters of this book describe and extend the scope of reinforcement learning. The remaining 11 chapters show that there is already wide usage in numerous fields. Reinforcement learning can tackle control tasks that are too complex for traditional, hand-designed, non-learning controllers. As learning computers can deal with technical complexities, the tasks of human operators remain to specify goals on increasingly higher levels. This book shows that reinforcement learning is a very dynamic area in terms of theory and applications and it shall stimulate and encourage new research in this field.
Reviews