Book Image

Deep Reinforcement Learning Hands-On - Second Edition

By : Maxim Lapan
Book Image

Deep Reinforcement Learning Hands-On - Second Edition

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

OpenAI Universe

The core idea underlying OpenAI Universe (available at https://github.com/openai/universe) is to wrap general GUI applications into an RL environment using the same core classes provided by Gym. To achieve this, it uses the VNC protocol to connect with the VNC server running inside the Docker (a standard method running lightweight containers) container, exposing the mouse and keyboard actions to the RL agent and providing the GUI application image as an observation.

The reward is provided by an external small rewarder daemon running inside the same container and giving the agent the scalar reward value based on this rewarder judgement. It is possible to launch several containers locally, or over the network, to gather episodes data in parallel, in the same way that we started several Atari emulators to increase the convergence of the asynchronous advantage actor-critic (A3C) method in Chapter 13, Asynchronous Advantage Actor-Critic. The architecture is illustrated...