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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Index

The paper's results

The final result published in the paper is quite impressive. After 44 hours of training on a machine with three GPUs, the network learned how to solve cubes at the same level as (and sometimes better than) human-crafted solvers. The final model has been compared against the two solvers described earlier: the Kociemba two-stage solver and Korf. The method proposed in the paper is named DeepCube.

To compare efficiency, 640 randomly scrambled cubes were used in all the methods. The depth of the scramble was 1,000 moves. The time limit for the solution was an hour and both the DeepCube and Kociemba solvers were able to solve all of the cubes within the limit. The Kociemba solver is very fast, and its median solution time is just one second, but due to the hardcoded rules implemented in the method, its solutions are not always the shortest ones.

The DeepCube method took much more time, with the median time being about 10 minutes, but it was able to match the...