Book Image

Deep Reinforcement Learning Hands-On

By : Maxim Lapan
Book Image

Deep Reinforcement Learning Hands-On

By: Maxim Lapan

Overview of this book

Deep Reinforcement Learning Hands-On is a comprehensive guide to the very latest DL tools and their limitations. You will evaluate methods including Cross-entropy and policy gradients, before applying them to real-world environments. Take on both the Atari set of virtual games and family favorites such as Connect4. The book provides an introduction to the basics of RL, giving you the know-how to code intelligent learning agents to take on a formidable array of practical tasks. Discover how to implement Q-learning on 'grid world' environments, teach your agent to buy and trade stocks, and find out how natural language models are driving the boom in chatbots.
Table of Contents (23 chapters)
Deep Reinforcement Learning Hands-On
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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 a gist of complicated things in simple words and vivid examples. His current areas of interest lie in practical applications of Deep Learning, such as Deep Natural Language Processing and Deep Reinforcement Learning.

Maxim lives in Moscow, Russian Federation, with his family, and he works for an Israeli start-up as a Senior NLP developer.

About the reviewers

Basem O. F. Alijla received his Ph.D. degree in intelligent systems from USM, Malaysia, in 2015. He is currently an assistant professor with Software Development Department, IUG in Palestine. He has authored number of technical papers published in journals and international conferences. His current research interest include, Optimization, Machine Learning, and Data mining.

Oleg Vasilev is a professional with a background in Computer Science and Data Engineering. His university program is Applied Mathematics and Informatics in NRU HSE, Moscow, with a major in Distributed Systems. He is a staff member on a Git-course, Practical_RL and Practical_DL, taught on-campus in HSE and YSDA. Oleg's previous work experience includes working in Dialog Systems Group, Yandex, as Data Scientist. He currently holds a position of Vice President of Infrastructure Management in GoTo Lab, an educational corporation, and he works for Digital Contact as a software engineer.

Mikhail Yurushkin holds a PhD in Applied Mathematics. His areas of research are high performance computing and optimizing compilers development. He was involved in the development of a state-of-the-art optimizing parallelizing compiler system. Mikhail is a senior lecturer at SFEDU university, Rostov on Don, Russia. He teaches advanced DL courses, namely Computer Vision and NLP. Mikhail has worked for over 7 years in cross-platform native C++ development, machine learning, and deep learning. Now he works as an individual consultant in ML/DL fields.

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