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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
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Efficient Mechanisms with Dynamic Populations and Dynamic Types
TL;DR: A mechanism is obtained that is efficient and within-period ex post incentive compatible for arrival processes in which future arrivals are conditionally independent of past arrivals given the actions of the center, and shown to be payoff equivalent at arrival to the online VCG mechanism.
Reference EntryDOI
Reinforcement Learning Algorithms for MDPs
TL;DR: This article focuses on a few selected algorithms of reinforcement learning which build on the powerful theory of dynamic programming.
Journal ArticleDOI
Algorithmic Analysis of Qualitative and Quantitative Termination Problems for Affine Probabilistic Programs
TL;DR: This article focuses on algorithmic synthesis of linear ranking-supermartingales over affine probabilistic programs (Apps) with both angelic and demonic non-determinism, and shows that the membership problem of LRApp can be decided in polynomial time and the NP-hardness result holds already for Apps without probability and demonic Non-Determinism.
Journal ArticleDOI
A reinforcement learning approach for waterflooding optimization in petroleum reservoirs
TL;DR: Waterflooding optimization problem has been defined and formulated in the framework of Reinforcement Learning (RL) methodology, which is known as a derivative-free and also model-free optimization approach and shows that by properly adjustment of the rewarding policies in the learning process, diverse forms of multi-objective optimization problems can be formulated, analyzed and solved.
Proceedings ArticleDOI
An efficient adaptive focused crawler based on ontology learning
TL;DR: An intelligent focused crawler algorithm in which ontology is embedded to evaluate the page's relevance to the topic and can evolve the ontology automatically during crawl process, compared with other algorithms using domain knowledge.
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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.
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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.
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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.