Deep Reinforcement Learning

Frontiers of Artificial Intelligence

AvMohit Sewak

Inbunden, Engelska, 2019

1 760 kr

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Beskrivning

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.

Produktinformation

Utgivningsdatum:2019-07-11
Mått:155 x 235 x undefined mm
Format:Inbunden
Språk:Engelska
Antal sidor:203
Förlag:Springer Verlag, Singapore
ISBN:9789811382840

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Mer om författaren

Mr. Sewak has been the Lead Data Scientist/Analytics Architect for a number of important international AI/DL/ML software and industry solutions and has also been involved in providing solutions and research for a series of cognitive features for IBM Watson Commerce. He has 14 years of experience working as a solutions architect using technologies like TensorFlow, Torch, Caffe, Theano, Keras, Open AI, SpaCy, Gensim, NLTK, Watson, SPSS, Spark, H2O, Kafka, ES, and others.

Innehållsförteckning

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.