tag :: Theory, | BibSonomy

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1Funderstanding
Fun site with links to articles on many learning theories including Piaget, Constructivism, Behavioralism, Brain Based Learning, Social Cognition, Emotional Inteligence, Social Learning Theory, and more.
9 years ago by @leon92183
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Learning
Learning,
Styles
Theories,
Theory,
LearningLearning,StylesTheories,Theory,
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1Another Point of View: The Eight Principles Of Incompetence
Fantastic, must read human theories
18 years ago by @revellian
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life
new-age
theory,
lifenew-agetheory,
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1Efficient Approximate Planning in Continuous Space Markovian Decision Problems
{. Szepesvári. AI Communications, 13 (3): 163--176 (2001)
5 years ago by @csaba
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Lipschitz-continuous
MDPs,
Monte-Carlo
analysis
bounds,
complexity
continuous-space
curse
dimensionality,
methods,
of
performance
theory,
Lipschitz-continuousMDPs,Monte-Carloanalysisbounds,complexitycontinuous-spacecursedimensionality,methods,ofperformancetheory,
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1Reinforcement learning: Theory and practice
{. Szepesvári. Proceedings of the 2nd Slovak Conference on Artificial Neural Networks (SCANN'98), page 29--39. (1998)
5 years ago by @csaba
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MDP
information,
learning,
partial
reinforcement
theory,
transformations
MDPinformation,learning,partialreinforcementtheory,transformations
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1Generalized Markov Decision Processes: Dynamic-programming and reinforcement-learning algorithms
{. Szepesvári, and M. Littman. CS-96-11. Brown University, Department of Computer Science, Providence, RI, (November 1996)
5 years ago by @csaba
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MDPs,
approximation
asymptotic
convergence,
finite
learning,
reinforcement
stochastic
theory,
MDPs,approximationasymptoticconvergence,finitelearning,reinforcementstochastictheory,
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1Fast Gradient-Descent Methods for Temporal-Difference Learning with Linear Function Approximation
R. Sutton, H. Maei, D. Precup, S. Bhatnagar, D. Silver, {. Szepesvári, and E. Wiewiora. ICML, page 993--1000. (2009)
5 years ago by @csaba
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GTD2,
TDC
algorithm,
approximation,
function
gradient
learning,
online
prediction,
reinforcement
stochastic
theory,
GTD2,TDCalgorithm,approximation,functiongradientlearning,onlineprediction,reinforcementstochastictheory,
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2Convergence Results for Single-Step On-Policy Reinforcement-Learning Algorithms
S. Singh, T. Jaakkola, M. Littman, and {. Szepesvári. Machine Learning, 38 (3): 287--308 (2000)
5 years ago by @csaba
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MDPs,
approximation
asymptotic
convergence,
finite
learning,
reinforcement
stochastic
theory,
MDPs,approximationasymptoticconvergence,finitelearning,reinforcementstochastictheory,
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1Q-learning Combined with Spreading: Convergence and Results
C. Ribeiro, and {. Szepesvári. Proceedings of ISRF-IEE International Conference: Intelligent and Cognitive Systems, Neural Networks Symposium, page 32--36. Tehran, Iran, (1996)
5 years ago by @csaba
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asymptotic
convergence
learning,
reinforcement
theory,
asymptoticconvergencelearning,reinforcementtheory,
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1REGO: Rank-based Estimation of Rényi Information using Euclidean Graph Optimization
B. Póczos, S. Kirshner, and {. Szepesvári. AISTATS, 9, page 852--859. (May 2010)
5 years ago by @csaba
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information
mutual
theory,
informationmutualtheory,
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1Training Parsers by Inverse Reinforcement Learning
G. Neu, and {. Szepesvári. Machine Learning, (2009)
5 years ago by @csaba
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application,
apprenticeship
gradient,
inverse
learning,
natural
prediction,
reinforcement
structured
survey
theory,
application,apprenticeshipgradient,inverselearning,naturalprediction,reinforcementstructuredsurveytheory,
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2Apprenticeship Learning using Inverse Reinforcement Learning and Gradient Methods
G. Neu, and {. Szepesvári. UAI, page 295--302. (2007)
5 years ago by @csaba
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application,
apprenticeship
gradient,
inverse
learning
learning,
natural
reinforcement
theory,
application,apprenticeshipgradient,inverselearninglearning,naturalreinforcementtheory,
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1Finite Time Bounds for Fitted Value Iteration
R. Munos, and {. Szepesvári. JMLR, (2008)
5 years ago by @csaba
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batch
bounds,
learning,
nonparametrics
performance
reinforcement
theory,
batchbounds,learning,nonparametricsperformancereinforcementtheory,
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1Finite Time Bounds for Sampling Based Fitted Value Iteration
R. Munos, and {. Szepesvári. ICML, page 881---886. (2005)
5 years ago by @csaba
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batch
bounds,
learning,
nonparametrics
performance
reinforcement
theory,
batchbounds,learning,nonparametricsperformancereinforcementtheory,
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1Convergent Temporal-Difference Learning with Arbitrary Smooth Function Approximation
H. Maei, {. Szepesvári, S. Bhatnagar, D. Silver, D. Precup, and R. Sutton. NIPS, page 1204--1212. (2009)
5 years ago by @csaba
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GTD
algorithm,
approximation,
function
gradient
learning,
networks,
neural
nonlinear
online
prediction,
reinforcement
stochastic
theory,
GTDalgorithm,approximation,functiongradientlearning,networks,neuralnonlinearonlineprediction,reinforcementstochastictheory,
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1Uncertainty, Performance and Model Dependency in Approximate Adaptive Nonlinear Control
M. French, {. Szepesvári, and E. Rogers. CDC, 3, page 3046 - 3051. San Diego, California, (1997)
5 years ago by @csaba
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Lyapunov
adaptive
bounds,
chain
control,
design,
feedback,
integrators,
matched
nonparametrics,
of
performance
stabilization
strict
theory,
uncertainty,
Lyapunovadaptivebounds,chaincontrol,design,feedback,integrators,matchednonparametrics,ofperformancestabilizationstricttheory,uncertainty,
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2A Generalized Reinforcement Learning Model: Convergence and Applications
M. Littman, and {. Szepesvári. ICML, page 310--318. (1996)
5 years ago by @csaba
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MDPs,
approximation
asymptotic
convergence,
finite
learning,
reinforcement
stochastic
theory,
MDPs,approximationasymptoticconvergence,finitelearning,reinforcementstochastictheory,
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1Learning Exercise Policies for American Options
Y. Li, {. Szepesvári, and D. Schuurmans. AISTATS, 5, page 352--359. (2009)
5 years ago by @csaba
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application
finance,
learning,
reinforcement
theory,
applicationfinance,learning,reinforcementtheory,
(0)
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1Performance of Nonlinear Approximate Adaptive Controllers
M. French, {. Szepesvári, and E. Rogers. Wiley, (2003)
5 years ago by @csaba
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Lyapunov
adaptive
approximation
control,
design,
function
theory,
Lyapunovadaptiveapproximationcontrol,design,functiontheory,
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1Uncertainty and Performance of Adaptive Controllers for Functionally Uncertain Output Feedback Systems
M. French, {. Szepesvári, and E. Rogers. CDC, page 4515--4520. Tampa, Florida, IEEE, (December 1998)
5 years ago by @csaba
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Lyapunov
adaptive
bounds,
control,
design,
feedback,
nonparametrics,
output
performance
stabilization
theory,
tracking,
Lyapunovadaptivebounds,control,design,feedback,nonparametrics,outputperformancestabilizationtheory,tracking,
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1Scaling of LQ Performance in Approximate Adaptive Designs
M. French, {. Szepesvári, and E. Rogers. Proceedings of the International Symposium on the Mathematical Theory of Networks and Systems (MTNS 2000), (2000)
5 years ago by @csaba
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Lyapunov
adaptive
approximation,
bounds,
chain
control,
design,
function
integrators,
multiresolution
nonparametrics
of
performance
theory,
tracking,
Lyapunovadaptiveapproximation,bounds,chaincontrol,design,functionintegrators,multiresolutionnonparametricsofperformancetheory,tracking,
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1An Asymptotic Scaling Analysis of LQ performance for an Approximate Adaptive Control Design
M. French, {. Szepesvári, and E. Rogers. Mathematics of Control, Signals and Systems, (2000)
5 years ago by @csaba
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Lyapunov
adaptive
approximation,
bounds,
chain
control,
design,
function
integrators,
multiresolution
nonparametrics
of
performance
theory,
tracking,
Lyapunovadaptiveapproximation,bounds,chaincontrol,design,functionintegrators,multiresolutionnonparametricsofperformancetheory,tracking,
(0)
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1LQ performance Bounds for Adaptive Output Feedback Controllers for Functionally Uncertain Systems
M. French, {. Szepesvári, and E. Rogers. Automatica, (2000)
5 years ago by @csaba
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Lyapunov
adaptive
bounds,
control,
design,
feedback,
nonparametrics,
output
performance
stabilization
theory,
tracking,
Lyapunovadaptivebounds,control,design,feedback,nonparametrics,outputperformancestabilizationtheory,tracking,
(0)
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BibSonomy

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1Funderstanding

1Another Point of View: The Eight Principles Of Incompetence

publications (hide)366
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1Efficient Approximate Planning in Continuous Space Markovian Decision Problems

1Reinforcement learning: Theory and practice

1Generalized Markov Decision Processes: Dynamic-programming and reinforcement-learning algorithms

1Fast Gradient-Descent Methods for Temporal-Difference Learning with Linear Function Approximation

2Convergence Results for Single-Step On-Policy Reinforcement-Learning Algorithms

1Q-learning Combined with Spreading: Convergence and Results

1REGO: Rank-based Estimation of Rényi Information using Euclidean Graph Optimization

1Training Parsers by Inverse Reinforcement Learning

2Apprenticeship Learning using Inverse Reinforcement Learning and Gradient Methods

1Finite Time Bounds for Fitted Value Iteration

1Finite Time Bounds for Sampling Based Fitted Value Iteration

1Convergent Temporal-Difference Learning with Arbitrary Smooth Function Approximation

1Uncertainty, Performance and Model Dependency in Approximate Adaptive Nonlinear Control

2A Generalized Reinforcement Learning Model: Convergence and Applications

1Learning Exercise Policies for American Options

1Performance of Nonlinear Approximate Adaptive Controllers

1Uncertainty and Performance of Adaptive Controllers for Functionally Uncertain Output Feedback Systems

1Scaling of LQ Performance in Approximate Adaptive Designs

1An Asymptotic Scaling Analysis of LQ performance for an Approximate Adaptive Control Design

1LQ performance Bounds for Adaptive Output Feedback Controllers for Functionally Uncertain Systems

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