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 
Reinforcement learning 

Markov Decision Processes 

Temporal difference learning 

Stochastic approximation 

Twotimescale stochastic approximation 

MonteCarlo methods 

Simulation optimization 

Function approximation 

Stochastic gradient methods 

Leastsquares methods 

Overfitting 

Biasvariance tradeoff 

Online learning 

Active learning 

Planning 

Simulation 

PAClearning 

Qlearning 

Actorcritic methods 

Policy gradient 

Natural gradient 
Bibliography 
Includes bibliographical references (pages 7388) 
Subject 
Reinforcement learning  Mathematical models


Machine learning.


Markov processes.


Markov Chains


Machine Learning


COMPUTERS  Enterprise Applications  Business Intelligence Tools.


COMPUTERS  Intelligence (AI) & Semantics.


Machine learning


Markov processes

Form 
Electronic book

ISBN 
9781608454938 

1608454932 

1608454924 

9781608454921 

9783031015519 

3031015517 
