Book Image

Deep Reinforcement Learning Hands-On. - Second Edition

By : Maxim Lapan
5 (2)
Book Image

Deep Reinforcement Learning Hands-On. - Second Edition

5 (2)
By: Maxim Lapan

Overview of this book

Deep Reinforcement Learning Hands-On, Second Edition is an updated and expanded version of the bestselling guide to the very latest reinforcement learning (RL) tools and techniques. It provides you with an introduction to the fundamentals of RL, along with the hands-on ability to code intelligent learning agents to perform a range of practical tasks. With six new chapters devoted to a variety of up-to-the-minute developments in RL, including discrete optimization (solving the Rubik's Cube), multi-agent methods, Microsoft's TextWorld environment, advanced exploration techniques, and more, you will come away from this book with a deep understanding of the latest innovations in this emerging field. In addition, you will gain actionable insights into such topic areas as deep Q-networks, policy gradient methods, continuous control problems, and highly scalable, non-gradient methods. You will also discover how to build a real hardware robot trained with RL for less than $100 and solve the Pong environment in just 30 minutes of training using step-by-step code optimization. In short, Deep Reinforcement Learning Hands-On, Second Edition, is your companion to navigating the exciting complexities of RL as it helps you attain experience and knowledge through real-world examples.
Table of Contents (28 chapters)
26
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27
Index

Going hardcore: CuLE

During the writing of this chapter, NVIDIA researchers published the paper and code for their latest experiments with porting the Atari emulator on GPU: Steven Dalton, Iuri Frosio, GPU-Accelerated Atari Emulation for Reinforcement Learning, 2019, arXiv:1907.08467. The code of their Atari port is called CuLE (CUDA Learning Environment) and is available on GitHub: https://github.com/NVlabs/cule.

According to their paper, by keeping both the Atari emulator and NN on the GPU, they were able to get Pong solved within one to two minutes and reach FPS of 50k (on the advantage actor-critic (A2C) method, which will be the subject of the next part of the book).

Unfortunately, at the time of writing, the code wasn't stable enough. I failed to make it work on my hardware, but I hope that when you read this, the situation will have already changed. In any case, this project shows a somewhat extreme, but very efficient, way to increase RL methods' performance...