Deep Reinforcement Learning: Frontiers of Artificial Intelligence
by Sewak, Mohit
This book starts by presenting the basics of reinforcement learning using highly intuitive and easy-to-understand examples and applications, and then introduces the cutting-edge research advances that make reinforcement learning capable of out-performing most state-of-art systems, and even humans in a number of applications. The book not only equips readers with an understanding of multiple advanced and innovative algorithms, but also prepares them to implement systems such as those created by …
Read More ↓Title Statements
- Full title :Deep Reinforcement Learning : Frontiers of Artificial Intelligence / Mohit Sewak.
Summary
This book starts by presenting the basics of reinforcement learning using highly intuitive and easy-to-understand examples and applications, and then introduces the cutting-edge research advances that make reinforcement learning capable of out-performing most state-of-art systems, and even humans in a number of applications. The book not only equips readers with an understanding of multiple advanced and innovative algorithms, but also prepares them to implement systems such as those created by Google Deep Mind in actual code. This book is intended for readers who want to both understand and apply advanced concepts in a field that combines the best of two worlds - deep learning and reinforcement learning - to tap the potential of 'advanced artificial intelligence for creating real-world applications and game-winning algorithms.
Read More ↓Edition Statements
- Publication Date:2019
Publication Statements
- Publisher(s):Springer
- Place of Publication:Singapore
- Language:eng
- Publication Date:2019
The Physical Object
- Description:1 online resource (xvii, 203 pages) : : illustrations (some color)
Edition Identifiers
- ISBN:9811382859, 9789811382857
- oclc:on1106167873
- LC Classification:Q325.6
- Dewey Decimal:005.11
Table of Contents
Introduction to Reinforcement Learning -- Mathematical and Algorithmic understanding of Reinforcement Learning -- Coding the Environment and MDP Solution -- Temporal Difference Learning, SARSA, and Q Learning -- Q Learning in Code -- Introduction to Deep Learning -- Implementation Resources -- Deep Q Network (DQN), Double DQN and Dueling DQN -- Double DQN in Code -- Policy-Based Reinforcement Learning Approaches -- Actor-Critic Models & the A3C -- A3C in Code -- Deterministic Policy Gradient and the DDPG -- DDPG in Code.
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