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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:
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
Citations
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Journal ArticleDOI
Dopamine, prediction error and associative learning: A model-based account
TL;DR: This paper distinguishes itself from existing accounts by offering novel predictions pertaining to the firing of dopamine neurons in various untested behavioral scenarios and offers a plausible link between formal notions of prediction error and accounts of disturbances of thought in schizophrenia.
Journal ArticleDOI
In Situ Quality Monitoring in AM Using Acoustic Emission: A Reinforcement Learning Approach
TL;DR: In this paper, the authors proposed to combine acoustic emission and reinforcement learning for in situ and real-time quality monitoring of additive manufacturing (AM) processes in commercial equipment and demonstrated that each level of quality produced unique acoustic signatures during the build that were recognized by the classifier.
Proceedings Article
Dynamic game balancing: an evaluation of user satisfaction
TL;DR: An evaluation by human players of dynamic game balancing approaches indicates that adaptive approaches are more effective, and enumerates some issues encountered in evaluating users' satisfaction, in the context of games, and depicts some learned lessons.
Proceedings ArticleDOI
Stochastic invariants for probabilistic termination
TL;DR: In this paper, the authors considered the probabilistic termination problem for linear-arithmetic programs with non-definite variables and introduced the notion of stochastic invariants, which are constraints along with a probability bound that the constraints hold.
Proceedings ArticleDOI
A reinforcement learning based routing protocol with QoS support for biomedical sensor networks
TL;DR: Simulation results show that RL-QRP performs well in terms of a number of QoS metrics and energy efficiency in various medical scenarios, and has been proved to fit well in dynamic environments.
References
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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.
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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.
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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.