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Reinforcement Learning: An Introduction

TLDR
This book provides a clear and simple account of the key ideas and algorithms of reinforcement learning, which ranges from the history of the field's intellectual foundations to the most recent developments and applications.
Abstract
Reinforcement learning, one of the most active research areas in artificial intelligence, is a computational approach to learning whereby an agent tries to maximize the total amount of reward it receives when interacting with a complex, uncertain environment. In Reinforcement Learning, Richard Sutton and Andrew Barto provide a clear and simple account of the key ideas and algorithms of reinforcement learning. Their discussion ranges from the history of the field's intellectual foundations to the most recent developments and applications. The only necessary mathematical background is familiarity with elementary concepts of probability. The book is divided into three parts. Part I defines the reinforcement learning problem in terms of Markov decision processes. Part II provides basic solution methods: dynamic programming, Monte Carlo methods, and temporal-difference learning. Part III presents a unified view of the solution methods and incorporates artificial neural networks, eligibility traces, and planning; the two final chapters present case studies and consider the future of reinforcement learning.

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The neuronal architecture of the mushroom body provides a logic for associative learning

TL;DR: The elucidation of the complement of neurons of the MB provides a comprehensive anatomical substrate from which one can infer a functional logic of associative olfactory learning and memory.
Journal ArticleDOI

Goals and habits in the brain.

TL;DR: This work reviews four generations of work in this tradition of experimental work in cognitive neuroscience and provides pointers to the forefront of the field’s fifth generation.
Journal ArticleDOI

Near-Optimal Reinforcement Learning in Polynomial Time

TL;DR: In this paper, the authors show that the number of actions required to approach the optimal return is lower bounded by the mixing time of the optimal policy (in the undiscounted case) or by the horizon time T in the discounted case.
Journal ArticleDOI

Deep Neural Networks: A New Framework for Modeling Biological Vision and Brain Information Processing

TL;DR: This work states that biologically faithful feedforward and recurrent computational models of how biological brains perform high-level feats of intelligence, including vision, are entering an exciting new era.
References
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Journal ArticleDOI

Long short-term memory

TL;DR: A novel, efficient, gradient based method called long short-term memory (LSTM) is introduced, which can learn to bridge minimal time lags in excess of 1000 discrete-time steps by enforcing constant error flow through constant error carousels within special units.
Book

Genetic algorithms in search, optimization, and machine learning

TL;DR: In this article, the authors present the computer techniques, mathematical tools, and research results that will enable both students and practitioners to apply genetic algorithms to problems in many fields, including computer programming and mathematics.
Journal ArticleDOI

Gradient-based learning applied to document recognition

TL;DR: In this article, a graph transformer network (GTN) is proposed for handwritten character recognition, which can be used to synthesize a complex decision surface that can classify high-dimensional patterns, such as handwritten characters.

Genetic algorithms in search, optimization and machine learning

TL;DR: This book brings together the computer techniques, mathematical tools, and research results that will enable both students and practitioners to apply genetic algorithms to problems in many fields.
Journal ArticleDOI

Pattern Recognition and Machine Learning

Radford M. Neal
- 01 Aug 2007 - 
TL;DR: This book covers a broad range of topics for regular factorial designs and presents all of the material in very mathematical fashion and will surely become an invaluable resource for researchers and graduate students doing research in the design of factorial experiments.
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