Book Image

Deep Reinforcement Learning Hands-On - Second Edition

By : Maxim Lapan
5 (1)
Book Image

Deep Reinforcement Learning Hands-On - Second Edition

5 (1)
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)
Other Books You May Enjoy


Historically, the PPO method came from the OpenAI team and it was proposed long after TRPO, which is from 2015. However, PPO is much simpler than TRPO, so we will start with it. The 2017 paper in which it was proposed is by John Schulman et. al., and it is called Proximal Policy Optimization Algorithms (arXiv:1707.06347).

The core improvement over the classic A2C method is changing the formula used to estimate the policy gradients. Instead of using the gradient of logarithm probability of the action taken, the PPO method uses a different objective: the ratio between the new and the old policy scaled by the advantages.

In math form, the old A2C objective could be written as . The new objective proposed by PPO is .

The reason behind changing the objective is the same as with the cross-entropy method covered in Chapter 4, The Cross-Entropy Method: importance sampling. However, if we just start to blindly maximize this value, it may lead to a very large update to the policy...