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Deep Reinforcement Learning Hands-On. - Second Edition

You're reading from  Deep Reinforcement Learning Hands-On. - Second Edition

Product type Book
Published in Jan 2020
Publisher Packt
ISBN-13 9781838826994
Pages 826 pages
Edition 2nd Edition
Languages
Concepts
Author (1):
Maxim Lapan Maxim Lapan
Profile icon Maxim Lapan
LinkedIn
Maxim has been working as a software developer for more than 20 years and was involved in various areas: distributed scientific computing, distributed systems and big data processing. Since 2014 he is actively using machine and deep learning to solve practical industrial tasks, such as NLP problems, RL for web crawling and web pages analysis. He has been living in Germany with his family.
See other products by Maxim Lapan

Table of Contents (28) Chapters

Preface What Is Reinforcement Learning? OpenAI Gym Deep Learning with PyTorch The Cross-Entropy Method Tabular Learning and the Bellman Equation Deep Q-Networks Higher-Level RL Libraries DQN Extensions Ways to Speed up RL Stocks Trading Using RL Policy Gradients – an Alternative The Actor-Critic Method Asynchronous Advantage Actor-Critic Training Chatbots with RL The TextWorld Environment Web Navigation Continuous Action Space RL in Robotics Trust Regions – PPO, TRPO, ACKTR, and SAC Black-Box Optimization in RL Advanced Exploration Beyond Model-Free – Imagination AlphaGo Zero RL in Discrete Optimization Multi-agent RL Other Books You May Enjoy
Index

Black-box methods

To begin with, let's discuss the whole family of black-box methods and how it differs from what we've covered 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 assumptions about the differentiability, the value function, the 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 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, ), 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...

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Authors (1)

Profile icon Maxim Lapan
LinkedIn
Maxim has been working as a software developer for more than 20 years and was involved in various areas: distributed scientific computing, distributed systems and big data processing. Since 2014 he is actively using machine and deep learning to solve practical industrial tasks, such as NLP problems, RL for web crawling and web pages analysis. He has been living in Germany with his family.
See other products by Maxim Lapan

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