Description 
1 online resource (xii, 89 pages) : illustrations 
Series 
Synthesis lectures on artificial intelligence and machine learning, 19394616 ; #9 

Synthesis lectures on artificial intelligence and machine learning ; #9

Contents 
1. Markov decision processes  Preliminaries  Markov decision processes  Value functions  Dynamic programming algorithms for solving MDPs 
Summary 
Reinforcement learning is a learning paradigm concerned with learning to control a system so as to maximize a numerical performance measure that expresses a longterm objective. What distinguishes reinforcement learning from supervised learning is that only partial feedback is given to the learner about the learner's predictions. Further, the predictions may have long term effects through influencing the future state of the controlled system. Thus, time plays a special role. The goal in reinforcement learning is to develop efficient learning algorithms, as well as to understand the algorithms' merits and limitations. Reinforcement learning is of great interest because of the large number of practical applications that it can be used to address, ranging from problems in artificial intelligence to operations research or control engineering. In this book, we focus on those algorithms of reinforcement learning that build on the powerful theory of dynamic programming. We give a fairly comprehensive catalog of learning problems, describe the core ideas, note a large number of state of the art algorithms, followed by the discussion of their theoretical properties and limitations 
Analysis 
Active learning 

Actorcritic methods 

Biasvariance tradeoff 

Function approximation 

Leastsquares methods 

Markov Decision Processes 

MonteCarlo methods 

Natural gradient 

Online learning 

Overfitting 

PAClearning 

Planning 

Policy gradient 

Qlearning 

Reinforcement learning 

Simulation 

Simulation optimization 

Stochastic approximation 

Stochastic gradient methods 

Temporal difference learning 

Twotimescale stochastic approximation 
Bibliography 
Includes bibliographical references (pages 7388) 
Subject 
Machine learning.


Markov processes.


Reinforcement learning  Mathematical models.

Form 
Electronic book

ISBN 
1608454932 (electronic bk.) 

9781608454938 (electronic bk.) 
