Deep Reinforcement Learning: Frontiers of Artificial Intelligence
- 2h 51m
- Mohit Sewak
- Springer
- 2019
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.
About the Author
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.
In this Book
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Introduction to Reinforcement Learning: The Intelligence Behind the AI Agent
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Mathematical and Algorithmic Understanding of Reinforcement Learning: The Markov Decision Process and Solution Approaches
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Coding the Environment and MDP Solution: Coding the Environment, Value Iteration, and Policy Iteration Algorithms
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Temporal Difference Learning, SARSA, and Q-Learning: Some Popular Value Approximation Based Reinforcement Learning Approaches
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Q-Learning in Code: Coding the Off-Policy Q-Learning Agent and Behavior Policy
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Introduction to Deep Learning: Enter the World of Modern Machine Learning
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Implementation Resources: Training Environments and Agent Implementation Libraries
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Deep Q Network (DQN), Double DQN, and Dueling DQN: A Step Towards General Artificial Intelligence
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Double DQN in Code: Coding the DDQN with Epsilon-Decay Behavior Policy
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Policy-Based Reinforcement Learning Approaches: Stochastic Policy Gradient and the REINFORCE Algorithm
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Actor-Critic Models and the A3C: The Asynchronous Advantage Actor-Critic Model
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A3C in Code: Coding the Asynchronous Advantage Actor-Critic Agent
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Deterministic Policy Gradient and the DDPG: Deterministic-Policy-Gradient-Based Approaches
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DDPG in Code: Coding the DDPG Using High-Level Wrapper Libraries
