Deep Reinforcement Learning Hands-On: Apply modern RL methods to practical problems of chatbots, robotics, discrete optimization, web automation, and more, 2nd Edition
Author:   Maxim Lapan
Publisher:   Packt Publishing Limited
Edition:   2nd Revised edition
ISBN:  

9781838826994


Pages:   826
Publication Date:   31 January 2020
Format:   Paperback
Availability:   Available To Order   Availability explained
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Deep Reinforcement Learning Hands-On: Apply modern RL methods to practical problems of chatbots, robotics, discrete optimization, web automation, and more, 2nd Edition


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Full Product Details

Author:   Maxim Lapan
Publisher:   Packt Publishing Limited
Imprint:   Packt Publishing Limited
Edition:   2nd Revised edition
ISBN:  

9781838826994


ISBN 10:   1838826998
Pages:   826
Publication Date:   31 January 2020
Audience:   Professional and scholarly ,  Professional & Vocational
Format:   Paperback
Publisher's Status:   Active
Availability:   Available To Order   Availability explained
We have confirmation that this item is in stock with the supplier. It will be ordered in for you and dispatched immediately.

Table of Contents

Table of Contents What Is Reinforcement Learning? OpenAI Gym Deep Learning with PyTorch The Cross-Entropy Method Tabular Learning and the Bellman Equation Deep Q-Networks Higher-Level RL libraries DQN Extensions Ways to Speed up RL Stocks Trading Using RL Policy Gradients - an Alternative The Actor-Critic Method Asynchronous Advantage Actor-Critic Training Chatbots with RL The TextWorld environment Web Navigation Continuous Action Space RL in Robotics Trust Regions - PPO, TRPO, ACKTR, and SAC Black-Box Optimization in RL Advanced exploration Beyond Model-Free - Imagination AlphaGo Zero RL in Discrete Optimisation Multi-agent RL

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Author Information

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 lies 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 non-HPC systems, he is able to explain a number of complicated concepts in simple words and vivid examples. His current areas of interest are 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.

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