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
Citations
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Journal ArticleDOI
Conjectural Equilibrium in Multiagent Learning
Michael P. Wellman,Junling Hu +1 more
TL;DR: This work presents a generic multiagent exchange situation, in which competitive behavior constitutes a conjectural equilibrium, and introduces an agent that executes a more sophisticated strategic learning strategy, building a model of the response of other agents.
Journal ArticleDOI
Selective impairment of prediction error signaling in human dorsolateral but not ventral striatum in Parkinson's disease patients: evidence from a model-based fMRI study
TL;DR: Assessment of striatal error signals in Parkinson's disease patients performing an RL task during fMRI scanning shows that error signals were preserved in ventral striatum of PD patients, but impaired in dorsolateral striatum, a pattern reflecting the known selective anatomical degeneration of dopamine nuclei in PD.
Dissertation
Development and application of hyperheuristics to personnel scheduling
TL;DR: A hyperheuristic which uses a choice function in order to select which low-level heuristic to apply at each decision point appears to be superior to other hyperheuristics considered and produces results competitive with those obtained using other sophisticated means.
A unifying framework for computational reinforcement learning theory
Michael L. Littman,Lihong Li +1 more
TL;DR: This thesis is that the KWIK learning model provides a flexible, modularized, and unifying way for creating and analyzing reinforcement-learning algorithms with provably efficient exploration and facilitates the development of new algorithms with smaller sample complexity, which have demonstrated empirically faster learning speed in real-world problems.
DissertationDOI
Policy-Gradient Algorithms for Partially Observable Markov Decision Processes
TL;DR: This thesis develops several improved algorithms for learning policies with memory in an infinite-horizon setting including an application written for the Bunyip cluster that won the international Gordon-Bell prize for price/performance in 2001.
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.