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Reinforcement Learning: A Survey
TLDR
A survey of reinforcement learning from a computer science perspective can be found in this article, where the authors discuss the central issues of RL, including trading off exploration and exploitation, establishing the foundations of RL via Markov decision theory, learning from delayed reinforcement, constructing empirical models to accelerate learning, making use of generalization and hierarchy, and coping with hidden state.Abstract:Â
This paper surveys the field of reinforcement learning from a computer-science perspective. It is written to be accessible to researchers familiar with machine learning. Both the historical basis of the field and a broad selection of current work are summarized. Reinforcement learning is the problem faced by an agent that learns behavior through trial-and-error interactions with a dynamic environment. The work described here has a resemblance to work in psychology, but differs considerably in the details and in the use of the word ``reinforcement.'' The paper discusses central issues of reinforcement learning, including trading off exploration and exploitation, establishing the foundations of the field via Markov decision theory, learning from delayed reinforcement, constructing empirical models to accelerate learning, making use of generalization and hierarchy, and coping with hidden state. It concludes with a survey of some implemented systems and an assessment of the practical utility of current methods for reinforcement learning.read more
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Dissertation
Explorations in efficient reinforcement learning
TL;DR: Reinforcement learning methods are described which can solve sequential decision making problems by learning from trial and error and different categories of problems are described and new methods for solving them are introduced.
Proceedings Article
Adaptive treatment of epilepsy via batch-mode reinforcement learning
TL;DR: Recent techniques from the reinforcement learning literature are applied to learn an optimal stimulation policy using labeled training data from animal brain tissues for the treatment of epilepsy, and it is shown that these methods are an effective means of reducing tile incidence of seizures, while also minimizing the amount of stimulation applied.
Proceedings ArticleDOI
Sequential cost-sensitive decision making with reinforcement learning
TL;DR: A novel approach to sequential decision making based on the reinforcement learning framework is proposed to learn decision rules that optimize a sequence of cost-sensitive decisions so as to maximize the total benefits accrued over time.
Journal ArticleDOI
Learning in groups of traffic signals
TL;DR: This paper investigates the task of multiagent reinforcement learning for control of traffic signals in two situations: agents act individually and agents can be ''tutored'', meaning that another agent with a broader sight will recommend a joint action.
Proceedings Article
Shuffling a stacked deck: the case for partially randomized ranking of search engine results
TL;DR: It is shown that a modest amount of randomness leads to improved search results, in the context of an economic objective function based on aggregate result quality amortized over time.
References
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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
Machine learning
TL;DR: Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis.
Book
Markov Decision Processes: Discrete Stochastic Dynamic Programming
TL;DR: Puterman as discussed by the authors provides a uniquely up-to-date, unified, and rigorous treatment of the theoretical, computational, and applied research on Markov decision process models, focusing primarily on infinite horizon discrete time models and models with discrete time spaces while also examining models with arbitrary state spaces, finite horizon models, and continuous time discrete state models.
Book
Dynamic Programming and Optimal Control
TL;DR: The leading and most up-to-date textbook on the far-ranging algorithmic methododogy of Dynamic Programming, which can be used for optimal control, Markovian decision problems, planning and sequential decision making under uncertainty, and discrete/combinatorial optimization.
Book
Parallel and Distributed Computation: Numerical Methods
TL;DR: This work discusses parallel and distributed architectures, complexity measures, and communication and synchronization issues, and it presents both Jacobi and Gauss-Seidel iterations, which serve as algorithms of reference for many of the computational approaches addressed later.