Descriptions
An Introduction to Deep Reinforcement Learning, The current era of artificial intelligence and machine learning (AIML) tools has transformed the workings of vast swaths of our private, working, and social lives beyond recognition. These tools can solve many problems more efficiently than humans, allowing machines and related systems to reason and infer almost like humans, which has deep intellectual and philosophical implications. Machine learning is broadly classified into supervised, unsupervised, and deep reinforcement learning (DRL), with DRL coming closest to how humans reason. Innovations in DRL have led to many useful applications.
This book covers most areas of deep reinforcement learning, with a special focus on its mathematical and algorithmic foundations. Undergraduate and early graduate students will find it a valuable guide to the fast-developing field of DRL and its numerous applications in both technical and social contexts.
Book features
- Understand the foundations and classifications of machine learning, including supervised, unsupervised, and deep reinforcement learning.
- Explore the mathematical and algorithmic principles underlying deep reinforcement learning.
- Gain insight into single-agent and multi-agent reinforcement learning algorithms.
- Learn about recent developments and applications of deep reinforcement learning.
- Apply DRL concepts to both technical and social contexts.
Who this Book is for
- Undergraduate and early graduate students interested in deep reinforcement learning.
- Researchers and practitioners seeking a foundational understanding of DRL.
- Engineers and computer scientists applying DRL in technical or social domains.
- Anyone looking for a comprehensive introduction to the mathematical and algorithmic aspects of DRL.
Specificatoin of An Introduction to Deep Reinforcement Learning
- Publisher : Routledge
- Teacher : Vinod K. Mishra
- Language : English
- Level : All Levels
- Pages : 205
- Chapters: 7
- Format: EPUB
Content of An Introduction to Deep Reinforcement Learning
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