Deep Reinforcement Learning: Theory and Practice in Python Language
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Deep training with reinforcement (deep RL) combines two approaches to machine learning. In the course of such training, virtual agents learn to solve consistent problems of making decisions. Over the past decade, there have been a lot of extraordinary achievements in this area - from single -user and multi -user games, such as the Go and video games of Atari and Dota 2, to robotics.
This book is an introduction into deep training with reinforcement, uniquely combining theory and practice. The authors begin the narrative with basic information, then explain in detail the theory of deep RL algorithms, demonstrate their implementations using the example of the SLM Lab software library and finally describe the practical aspects of the use of deep RL.
The management is ideal for both students studying computer sciences and for software developers who are familiar with the basic principles of machine learning and know Python
This book is an introduction into deep training with reinforcement, uniquely combining theory and practice. The authors begin the narrative with basic information, then explain in detail the theory of deep RL algorithms, demonstrate their implementations using the example of the SLM Lab software library and finally describe the practical aspects of the use of deep RL.
The management is ideal for both students studying computer sciences and for software developers who are familiar with the basic principles of machine learning and know Python
Author:
Author:Грессер Лаура, Кенг Ван Лун
Cover:
Cover:Soft
Category:
- Category:Computer & Technology
Publication language:
Publication Language:Russian
Paper:
Paper:Offset
Age restrictions:
Age restrictions:16+
ISBN:
ISBN:978-5-4461-1699-7
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