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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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Reinforcement-Learning-Based Robust Controller Design for Continuous-Time Uncertain Nonlinear Systems Subject to Input Constraints

TL;DR: A novel RL-based robust adaptive control algorithm is developed for a class of continuous-time uncertain nonlinear systems subject to input constraints that is converted to the constrained optimal control problem with appropriately selecting value functions for the nominal system.
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Artificial Intelligence techniques: An introduction to their use for modelling environmental systems

TL;DR: The techniques covered are case-based reasoning, rule-based systems, artificial neural networks, fuzzy models, genetic algorithms, cellular automata, multi-agent systems, swarm intelligence, reinforcement learning and hybrid systems.
Journal ArticleDOI

Contributions of the striatum to learning, motivation, and performance: an associative account.

TL;DR: This work considers empirical data from human and animal studies, as well as theoretical notions from both the psychological and computational literatures, and concludes that striatal sub-regions differ most clearly in terms of the associations being encoded in each region.
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Developmental robotics, optimal artificial curiosity, creativity, music, and the fine arts

TL;DR: It is pointed out how the fine arts can be formally understood as a consequence of the basic principle: given some subjective observer, great works of art and music yield observation histories exhibiting more novel, previously unknown compressibility/regularity/predictability than lesser works, thus deepening the observer’s understanding of the world and what is possible in it.
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The Role of Human Orbitofrontal Cortex in Value Comparison for Incommensurable Objects

TL;DR: It is demonstrated that orbitofrontal activation reflects the difference in subjective value between available options, an effect evident across valuation for both gains and losses, which indicates that orbit ofrontal cortex plays a pivotal role in valuation for incommensurable goods.
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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