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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Posted Content
Sequential Bayesian optimal experimental design via approximate dynamic programming
Xun Huan,Youssef M. Marzouk +1 more
TL;DR: This paper rigorously formulate the general sequential optimal experimental design (sOED) problem as a dynamic program, adopting a Bayesian formulation with an information theoretic design objective, and develops new numerical approaches for nonlinear design with continuous parameter, design, and observation spaces.
Journal ArticleDOI
Quantum robot: structure, algorithms and applications
TL;DR: The theoretical results show that quantum robots using quantum searching algorithms can reduce the complexity of the search problem from O($N^2)$ in classical robots to O ($N\sqrt N)$.
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Intelligent dynamic control policies for serial production lines
TL;DR: In this paper, a simulation-based optimization technique called Reinforcement Learning (RL) was applied on a four-station serial line to find a dynamic control policy via intelligent agents.
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Context-Aware Intelligence in Resource-Constrained IoT Nodes: Opportunities and Challenges
TL;DR: An academic perspective of the problem is provided, starting with a survey of recent advances in intelligent sensing, computation, communication, and energy management for resource-constrained IoT sensor nodes and leading to a future outlook and needs.
Proceedings ArticleDOI
Trust based knowledge outsourcing for semantic Web agents
Li Ding,Lina Zhou,Tim Finin +2 more
TL;DR: The term trust justification is used to describe the process in which an agent integrates the beliefs of other agents, trust information, and its own beliefs to update its trust model, and the results of simulation experiments of the use and evolution of trust in multiagent systems are described.
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.