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
Other Books You May Enjoy

Correlation and sample efficiency

One of the approaches to improving the stability of the Policy Gradient (PG) family of methods is to use multiple environments in parallel. The reason behind this is the fundamental problem we discussed in Chapter 6, Deep Q-Networks, when we talked about the correlation between samples, which breaks the independent and identically distributed (i.i.d) assumption, which is critical for Stochastic Gradient Descent (SGD) optimization. The negative consequence of such correlation is very high variance in gradients, which means that our training batch contains very similar examples, all of them pushing our network in the same direction. However, this may be totally the wrong direction in the global sense, as all those examples could be from one single lucky or unlucky episode.

With our Deep Q-Network (DQN), we solved the issue by storing a large amount of previous states in the replay buffer and sampling our training batch from this buffer. If the buffer is large...