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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 ArticleDOI

FALCON: a fusion architecture for learning, cognition, and navigation

TL;DR: The proposed cognitive model, called FALCON, enables an autonomous agent to adapt and function in a dynamic environment and is able to adapt amazingly well and learns rapidly through it's interaction with the environment in an online and incremental manner.
Dissertation

Exploration and Inference in Learning from Reinforcement

TL;DR: This thesis presents a number of novel extensions to existing techniques for controlling exploration and inference in reinforcement learning, and explains how the exploration problem can be viewed as a form of the well-studied inference problem and how the optimal solution can in principle be found using dynamic programming methods.
Posted Content

Learning How to Communicate in the Internet of Things: Finite Resources and Heterogeneity

TL;DR: A number of emerging learning frameworks suitable for IoT applications are presented and the different resource capabilities of IoT devices are mapped to different levels of rationality in cognitive hierarchy theory, thus enabling the IoT devices to use different learning frameworks depending on their available resources.
Proceedings ArticleDOI

Reward Shaping in Episodic Reinforcement Learning

TL;DR: A unifying analysis of potential-based reward shaping which leads to new theoretical insights into reward shaping in both model-free and model-based algorithms, as well as in multi-agent reinforcement learning.
Journal ArticleDOI

A theoretical account of cognitive effects in delay discounting.

TL;DR: A potential mechanism for delay discounting such that discounting emerges from a search process that is trying to determine what rewards will be available in the future, and explains why improving cognitive resources such as working memory slows discounting, by improving searches and thereby making rewards easier to find.
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.
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