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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
Citations
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Journal ArticleDOI
Monte Carlo hyper-heuristics for examination timetabling
TL;DR: A broad empirical analysis is performed comparing Monte Carlo based hyper-heuristics for solving capacitated examination timetabling problems and the experimental results show that simulated annealing with reheating as a hyper- heuristic move acceptance method has significant potential.
Journal ArticleDOI
Autonomous Learning of State Representations for Control: An Emerging Field Aims to Autonomously Learn State Representations for Reinforcement Learning Agents from Their Real-World Sensor Observations
Wendelin Böhmer,Jost Tobias Springenberg,Joschka Boedecker,Martin Riedmiller,Klaus Obermayer +4 more
TL;DR: An emerging field that aims for autonomous reinforcement learning directly on sensor-observations, and two approaches to learn intermediate state representations from previous experiences: deep auto-encoders and slow-feature analysis are reviewed.
Journal ArticleDOI
Efficient supervisory synthesis of large systems
TL;DR: This work presents efficient methods for reachability search based on symbolic computations using Binary Decision Diagrams and simple guidelines and quantities for separating hard and easy reachability problems are presented.
Dissertation
Socially guided machine learning
TL;DR: This thesis provides several contributions towards the understanding of this Socially Guided Machine Learning scenario by utilizing asymmetric interpretations of positive and negative feedback from a human partner to result in a more efficient and robust learning experience.
Relevant information in optimized persistence vs. progeny strategies
TL;DR: This work develops a general approach to treat problems that involve iterated games where utility is determined by iterated play of a strategy and where informational processing constraints limit the possible strategies.
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.