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

Atari experiments

The MountainCar environment is a nice and fast way to experiment with exploration methods, but to conclude the chapter, I've included Atari versions of the DQN and PPO methods with the exploration tweaks we described. As the primary environment, I've used Seaquest, which is a game where the submarine needs to shoot fish and enemy submarines, and save aquanauts. This game is not as famous as Montezuma's Revenge, but it still might be considered as medium-hard exploration, because to continue the game, you need to control the level of oxygen. When it becomes low, the submarine needs to rise to the surface for some time. Without this, the episode will end after 560 steps and with a maximum reward of 20. But once the agent learns how to replenish the oxygen, the game might continue almost infinitely and bring to the agent a 10k-100k score. Surprisingly, traditional exploration methods struggle with discovering this; normally, training gets stuck at 560...