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 command generation model

In this part of the chapter, we will extend our baseline model with an extra submodule that will generate commands that our DQN network should evaluate. In the baseline model, commands were taken from the admissible commands list, which was taken from the extended information from the environment. But maybe we can generate commands from the observation using the same techniques that we covered in the previous chapter.

The architecture of our new model is shown in Figure 15.12.

Figure 15.12: The architecture of the DQN with command generation

In comparison with Figure 15.3 from earlier in the chapter, there are several changes here. First of all, our preprocessor pipeline no longer accepts a command sequence in the input. The second difference is that the preprocessor's output now not only gets passed to the DQN model, but it also forks to the "Commands generator" submodule.

The responsibility of this new submodule is to produce...