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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
Learning action strategies for planning domains
TL;DR: The learned strategies are efficient, the solutions found by them are competitive with those of known heuristics for the domains, and transfer from small planning problems in the examples to larger ones in the test set is exhibited.
Journal ArticleDOI
Reward feedback accelerates motor learning
A.A. Nikooyan,Alaa A. Ahmed +1 more
TL;DR: The use of reward feedback is a promising approach to either supplement or substitute sensory feedback in the development of improved neurorehabilitation techniques and points to an important role played by reward in the motor learning process.
Journal ArticleDOI
An Evolutionary Dynamical Analysis of Multi-Agent Learning in Iterated Games
TL;DR: It is shown how evolutionary dynamics from Evolutionary Game Theory can help the developer of a MAS in good choices of parameter settings of the used RL algorithms and how the improved results for MAS RL in COIN, and a developed extension, are predicted by the ED.
Proceedings ArticleDOI
Responsive characters from motion fragments
James McCann,Nancy S. Pollard +1 more
TL;DR: An on-line character animation controller is described that assembles a motion stream from short motion fragments, choosing each fragment based on current player input and the previous fragment, and results indicating that occasional low-quality transitions between motion segments are crucial to high-quality on- line motion generation are provided.
Journal ArticleDOI
Design of Reinforcement Learning Parameters for Seamless Application of Adaptive Traffic Signal Control
TL;DR: This article investigates the following dimensions of the control problem: 1) RL learning methods, 2) traffic state representations, 3) action selection methods, 4) traffic signal phasing schemes, 5) reward definitions, and 6) variability of flow arrivals to the intersection.
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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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.
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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.