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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Index

Distributional policy gradients

As the last method of this chapter, we will take a look at the very recent paper by Gabriel Barth-Maron, Matthew W. Hoffman, and others, called Distributed Distributional Deterministic Policy Gradients, published in 2018 (https://arxiv.org/abs/1804.08617).

The full name of the method is distributed distributional deep deterministic policy gradients or D4PG for short. The authors proposed several improvements to the DDPG method to improve stability, convergence, and sample efficiency.

First of all, they adapted the distributional representation of the Q-value proposed in the paper by Marc G. Bellemare and others called A Distributional Perspective on Reinforcement Learning, published in 2017 (https://arxiv.org/abs/1707.06887). We discussed this approach in Chapter 8, DQN Extensions, when we talked about DQN improvements, so refer to it or to the original Bellemare paper for details. The core idea is to replace a single Q-value from the critic with...