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

Human demonstrations

The idea behind demonstrations is simple: to help our agent to discover the best way to solve the task, we show it some examples of actions that we think are required for the problem. Those examples could be not the best solution or 100% accurate, but they should be good enough to show the agent promising directions to explore.

In fact, this is a very natural thing to do and all human learning is based on some prior examples given by a teacher in class, your parents or other people. Those examples could be in a written form (recipe books) or given as demonstrations that you need to repeat several times to get it right (dance classes). Such forms of training are much more effective than random search. Just imagine how complicated and lengthy it would be to learn how to clean your teeth by trial-and-error alone. Of course, there is a danger from learning how to follow the demonstrations, which could be wrong or not the most efficient way to solve the problem, but overall...