Model-based reinforcement learning with model error and its application

Tajima, Y.; Onisawa, Takehisa

doi:10.1109/sice.2007.4421190

Cited by 1 publication

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“…Although this problem is inevitable for Model-based RL, there are few studies on this problem. This chapter gives careful consideration on the effects of the model error for Model-based RL and proposes a new approach, Model Error based Forward Planning Reinforcement Learning (ME-FPRL) (Tajima, et al, 2006)…”

Section: Machine Learning For Functional Brain Mappingmentioning

confidence: 99%

Application of Machine Learning

Zhang¹

2010

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His research includes relay protection of power system, wind power and nonlinear complex system theory.V PrefaceIn recent years many successful machine learning applications have been developed, ranging from data mining programs that learn to detect fraudulent credit card transactions, to information filtering systems that learn user's reading preferences, to autonomous vehicles that learn to drive on public highways. At the same time, machine learning techniques such as rule induction, neural networks, genetic learning, case-based reasoning, and analytic learning have been widely applied to real-world problems. Machine Learning employs learning methods which explore relationships in sample data to learn and infer solutions. Learning from data is a hard problem. It is the process of constructing a model from data. In the problem of pattern analysis, learning methods are used to find patterns in data. In the classification, one seeks to predict the value of a special feature in the data as a function of the remaining ones. A good model is one that can effectively be used to gain insights and make predictions within a given domain.General speaking, the machine learning techniques that we adopt should have certain properties for it to be efficient, for example, computational efficiency, robustness and statistical stability. Computational efficiency restricts the class of algorithms to those which can scale with the size of the input. As the size of the input increases, the computational resources required by the algorithm and the time it takes to provide an output should scale in polynomial proportion. In most cases, the data that is presented to the learning algorithm may contain noise. So the pattern may not be exact, but statistical. A robust algorithm is able to tolerate some level of noise and not affect its output too much. Statistical stability is a quality of algorithms that capture true relations of the source and not just some peculiarities of the training data. Statistically stable algorithms will correctly find patterns in unseen data from the same source, and we can also measure the accuracy of corresponding predictions.The goal of this book is to present the latest applications of machine learning, mainly include: speech recognition, traffic and fault classification, surface quality prediction in laser machining, network security and bioinformatics, enterprise credit risk evaluation, and so on. This book will be of interest to industrial engineers and scientists as well as academics who wish to pursue machine learning. The book is intended for both graduate and postgraduate students in fields such as computer science, cybernetics, system sciences, engineering, statistics, and social sciences, and as a reference for software professionals and practitioners. The wide scope of the book provides them with a good introduction to many application researches of machine learning, and it is also the source of useful bibliographical information.

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Section: Machine Learning For Functional Brain Mappingmentioning

confidence: 99%

Application of Machine Learning

Zhang¹

2010

View full text Add to dashboard Cite

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Model-based reinforcement learning with model error and its application

Cited by 1 publication

References 4 publications

Application of Machine Learning

Application of Machine Learning

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