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Deep Reinforcement Learning Hands-On: Apply modern RL methods, with deep Q-networks, value iteration, policy gradients, TRPO, AlphaGo Zero and more - Softcover
Synopsis
This practical guide will teach you how deep learning (DL) can be used to solve complex real-world problems.
Key Features
- Explore deep reinforcement learning (RL), from the first principles to the latest algorithms
- Evaluate high-profile RL methods, including value iteration, deep Q-networks, policy gradients, TRPO, PPO, DDPG, D4PG, evolution strategies and genetic algorithms
- Keep up with the very latest industry developments, including AI-driven chatbots
Book Description
Recent developments in reinforcement learning (RL), combined with deep learning...
About the Author
Maxim Lapan is a deep learning enthusiast and independent researcher. His background and 15 years' work expertise as a software developer and a systems architect lays from low-level Linux kernel driver development to performance optimization and design of distributed applications working on thousands of servers. With vast work experiences in big data, Machine Learning, and large parallel distributed HPC and nonHPC systems, he has a talent to explain...
"About this title" may belong to another edition of this title.
- PublisherPackt Publishing
- Publication date2018
- ISBN 10 1788834240
- ISBN 13 9781788834247
- BindingPaperback
- LanguageEnglish
- Number of pages546
- Rating
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Deep Reinforcement Learning Hands-On : Apply Modern RL Methods, with Deep Q-Networks, Value Iteration, Policy Gradients, TRPO, AlphaGo Zero and More
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Packt Publishing, Limited, 2018
ISBN 10: 1788834240
ISBN 13: 9781788834247
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