Open AccessPosted Content
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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AI for 5G: Research Directions and Paradigms
TL;DR: This study focuses on providing design paradigms including 5G network optimization, optimal resource allocation, 5G physical layer unified acceleration, end-to-end physical layer joint optimization, and so on based on an understanding of the key technologies in 5G.
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Robust chaos in neural networks
A. B. Potapov,M. K. Ali +1 more
TL;DR: In this paper, the authors consider the problem of creating a robust chaotic neural network and prove that chaos remains robust in a network of weakly connected neurons, and discuss ways to enhance the statistical properties of data generated by such a map or network.
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E-Learning: Challenges and Research Opportunities Using Machine Learning & Data Analytics
TL;DR: The field of e-learning is investigated in terms of definitions and characteristics and some of the works proposed in the literature to tackle the various challenges facing the different participants within this process are discussed.
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Integrating relevance feedback techniques for image retrieval using reinforcement learning
TL;DR: An image relevance reinforcement learning (IRRL) model for integrating existing RF techniques in a content-based image retrieval system is proposed and a concept digesting method is proposed to reduce the complexity of storage demand.
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Probabilistic Policy Reuse for inter-task transfer learning
TL;DR: This article uses several domains, as versions of a simulated RoboCup Keepaway problem, to show how Policy Reuse can be applied among domains by defining and using a mapping between their state and action spaces.
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.
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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.