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)
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The emulator and the model

In this section, we will cover the process of obtaining the policy that we will deploy on the hardware. As mentioned, we will use a physics emulator (PyBullet in our case) to simulate our robot. I won't describe in detail how to set up PyBullet, as it was covered in the previous chapter. Let's jump into the code and the model definition.

In the previous chapter, we used robot models already prepared for us, like Minitaur and HalfCheetah, which exposed the familiar and simple Gym interface with the reward, observations, and actions. Now we have custom hardware and have formulated our own reward objective, so we need to make everything ourselves. From my personal experiments, it turned out to be surprisingly complex to implement a low-level robot model and wrap it in a Gym environment. There were several reasons for that:

  • PyBullet classes are quite complicated and poorly designed from a software engineer point of view. They contain a lot...