The Reinforcement Learning Workshop: Learn how to apply cutting-edge reinforcement learning algorithms to a wide range of control problems Paperback – 18 Aug. 2020
by: Alessandro Palmas ,Emanuele Ghelfi ,Dr. Alexandra Galina Petre ,Mayur Kulkarni
Print Length 页数: 822 pages
ISBN-10: 1800200455
ISBN-13: 9781800200456
05 x 4.72 x 23.5 cm
Publisher finelybook 出版社: Packt Publishing (18 Aug. 2020)
Language 语言: English
Book Description
By finelybook
Start with the basics of reinforcement learning and explore deep learning concepts such as deep Q-learning,deep recurrent Q-networks,and policy-based methods with this practical guide
Various intelligent applications such as video games,inventory management software,warehouse robots,and translation tools use reinforcement learning (RL) to make decisions and perform actions that maximize the probability of the desired outcome. This book will help you to get to grips with the techniques and the algorithms for implementing RL in your machine learning models.
Starting with an introduction to RL,you’ll be guided through different RL environments and frameworks. You’ll learn how to implement your own custom environments and use OpenAI baselines to run RL algorithms. Once you’ve explored classic RL techniques such as Dynamic Programming,Monte Carlo,and TD Learning,you’ll understand when to apply the different deep learning methods in RL and advance to deep Q-learning. The book will even help you understand the different stages of machine-based problem-solving by: using DARQN on a popular video game Breakout. Finally,you’ll find out when to use a policy-based method to tackle an RL problem.
By the end of the The Reinforcement Learning Workshop,you’ll be equipped with the knowledge and skills needed to solve challenging machine learning problems using reinforcement learning.
What you will learn
Use OpenAI Gym as a framework to implement RL environments
Find out how to define and implement reward function
Explore Markov chain,Markov decision process,and the Bellman equation
Distinguish between Dynamic Programming,Monte Carlo,and Temporal Difference Learning
Understand the multi-armed bandit problem and explore various strategies to solve it
Build a deep Q model network for playing the video game Breakout
