Book Image

Deep Reinforcement Learning Hands-On

By : Maxim Lapan
Book Image

Deep Reinforcement Learning Hands-On

By: Maxim Lapan

Overview of this book

Deep Reinforcement Learning Hands-On is a comprehensive guide to the very latest DL tools and their limitations. You will evaluate methods including Cross-entropy and policy gradients, before applying them to real-world environments. Take on both the Atari set of virtual games and family favorites such as Connect4. The book provides an introduction to the basics of RL, giving you the know-how to code intelligent learning agents to take on a formidable array of practical tasks. Discover how to implement Q-learning on 'grid world' environments, teach your agent to buy and trade stocks, and find out how natural language models are driving the boom in chatbots.
Table of Contents (23 chapters)
Deep Reinforcement Learning Hands-On
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A2C using ACKTR

The third method that we'll compare uses a different approach to address SGD stability. In the paper by Yuhuai Wu and others called Scalable Trust-Region Method for Deep Reinforcement Learning Using Kronecker-Factored Approximation published in 2017 (arXiv:1708.05144), the authors combined the second-order optimization methods and trust region approach.

The idea of the second-order methods is to improve the traditional SGD by taking the second-order derivatives of the optimized function (in other words, its curvature) to improve the convergence of the optimization process. To make things more complicated, working with the second derivatives usually requires you to build and invert a Hessian matrix, which can be prohibitively large, so the practical methods typically approximate it in some way. This area is currently very active in research, as developing robust, scalable optimization methods is very important for the whole machine learning domain.

One of the second-order methods...