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

Black-box methods

In the beginning, let's discuss the whole family of methods and how they differ from what we've seen so far. Black-box optimization methods are the general approach to the optimization problem, when you treat the objective that you're optimizing as a black box, without any assumption about the differentiability, value function, smoothness of the objective, and so on. The only requirement that those methods expose is the ability to calculate the fitness function, which should give us the measure of suitability of a particular instance of the optimized entity at hand.

One of the simplest examples of the method in this family is random search, which is when you randomly sample the thing you're looking for (in the case of RL it's the policy ), then you check the fitness of this candidate, and if the result is good enough (according to some reward criteria), then you're done. Otherwise, you repeat the process again and again. Despite the simplicity and even naivety of this approach...