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Deep Reinforcement Learning Hands-On. - Second Edition

You're reading from  Deep Reinforcement Learning Hands-On. - Second Edition

Product type Book
Published in Jan 2020
Publisher Packt
ISBN-13 9781838826994
Pages 826 pages
Edition 2nd Edition
Languages
Concepts
Author (1):
Maxim Lapan Maxim Lapan
Profile icon Maxim Lapan
LinkedIn
Maxim has been working as a software developer for more than 20 years and was involved in various areas: distributed scientific computing, distributed systems and big data processing. Since 2014 he is actively using machine and deep learning to solve practical industrial tasks, such as NLP problems, RL for web crawling and web pages analysis. He has been living in Germany with his family.
See other products by Maxim Lapan

Table of Contents (28) Chapters

Preface 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 Optimization Multi-agent RL Other Books You May Enjoy
Index

Interactive fiction

As you have already seen, computer games are not only entertaining for humans, but also provide challenging problems for RL researchers due to the complicated observations and action spaces, long sequences of decisions to be made during the gameplay, and natural reward systems.

Arcade games like Atari 2600 are just one of many genres that the gaming industry has. From a historical perspective, the Atari 2600 platform peaked in popularity during the late 70s and early 80s. Then followed the era of Z80 and clones, which evolved into the period of the PC-compatible platforms and consoles we have now.

Over time, computer games continually become more complex, colorful, and detailed in terms of graphics, which inevitably increases hardware requirements. This trend makes it harder for RL researchers and practitioners to apply RL methods to the more recent games; for example, almost everybody can train an RL agent to solve an Atari game, but for StarCraft II, DeepMind...

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Authors (1)

Profile icon Maxim Lapan
LinkedIn
Maxim has been working as a software developer for more than 20 years and was involved in various areas: distributed scientific computing, distributed systems and big data processing. Since 2014 he is actively using machine and deep learning to solve practical industrial tasks, such as NLP problems, RL for web crawling and web pages analysis. He has been living in Germany with his family.
See other products by Maxim Lapan

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