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

Deterministic policy gradients

The next method that we will take a look at is called deterministic policy gradients, which is an actor-critic method but has a very nice property of being off-policy. The following is my very relaxed interpretation of the strict proofs. If you are interested in understanding the core of this method deeply, you can always refer to the article by David Silver and others called Deterministic Policy Gradient Algorithms, published in 2014 (http://proceedings.mlr.press/v32/silver14.pdf), and the paper by Timothy P. Lillicrap and others called Continuous Control with Deep Reinforcement Learning, published in 2015 (https://arxiv.org/abs/1509.02971).

The simplest way to illustrate the method is through comparison with the already familiar A2C method. In this method, the actor estimates the stochastic policy, which returns the probability distribution over discrete actions or, as we have just covered in the previous section, the parameters of normal distribution...