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)
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The A2C baseline

To establish the baseline results, we will use the A2C method in a very similar way to the code in the previous chapter.


The complete source is in files Chapter19/ and Chapter19/lib/ There are a few differences between this baseline and the version we used in the previous chapter. First of all, there are 16 parallel environments used to gather experience during the training. The second difference is the model structure and the way that we perform exploration. To illustrate them, let's look at the model and the agent classes.

Both the actor and critic are placed in the separate networks without sharing weights. They follow the approach used in the previous chapter, with our critic estimating the mean and the variance for the actions. However, now variance is not a separate head of the base network; it is just a single parameter of the model. This parameter will be adjusted during the training by SGD, but it doesn...