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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Adding an extra A to A2C

From the practical point of view, communicating with several parallel environments is simple. We already did this in the previous chapter, but it wasn't explicitly stated. In the A2C agent, we passed an array of Gym environments into the ExperienceSource class, which switched it into round-robin data gathering mode. This means that every time we ask for a transition from the experience source, the class uses the next environment from our array (of course, keeping the state for every environment). This simple approach is equivalent to parallel communication with environments, but with one single difference: communication is not parallel in the strict sense but performed in a serial way. However, samples from our experience source are shuffled. This idea is shown in the following diagram:

Figure 13.1: An agent training from multiple environments in parallel

This method works fine and helped us to get convergence in the A2C method, but it is still...