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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.

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Proceedings Article

Multi-resolution Exploration in Continuous Spaces

TL;DR: This work proposes a new methodology for representing uncertainty in continuous-state control problems by using a hierarchical mapping to identify regions of the state space that would benefit from additional samples and demonstrates MRE's broad utility by using it to speed up learning in a prototypical model-based and value-based reinforcement-learning method.
Proceedings Article

Heuristic selection of actions in multiagent reinforcement learning

TL;DR: A set of empirical evaluations were conducted for the proposed algorithm in a simplified simulator for the robot soccer domain, and experimental results show that even very simple heuristics enhances significantly the performance of the multiagent reinforcement learning algorithm.
Posted ContentDOI

Revealing neuro-computational mechanisms of reinforcement learning and decision-making with the hBayesDM package

TL;DR: An R package called hBayesDM (hierarchical Bayesian modeling of Decision-Making tasks), which offers computational modeling on an array of RLDM tasks and social exchange games and is expected to contribute to the dissemination of advanced modeling approaches and enable a wide range of researchers to easily perform computational psychiatric research within their populations.
Journal ArticleDOI

A Hybrid Privacy Protection Scheme in Cyber-Physical Social Networks

TL;DR: This work proposes a hybrid privacy-preserving scheme, which considers both location and identity privacy against a dynamic adversary, and establishes a game-based Markov decision process model, in which the user and the adversary are regarded as two players in a dynamic multistage zero-sum game.
Journal ArticleDOI

A framework for meta-level control in multi-agent systems

TL;DR: It is shown that meta-level control with bounded computational overhead allows complex agents to solve problems more efficiently than current approaches in dynamic open multi-agent 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.
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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.
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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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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.
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