Results 1  10
of
1,216,758
Sample Complexity of Multitask Reinforcement Learning
"... Transferring knowledge across a sequence of reinforcementlearning tasks is challenging, and has a number of important applications. Though there is encouraging empirical evidence that transfer can improve performance in subsequent reinforcementlearning tasks, there has been very little theoretical ..."
Abstract

Cited by 6 (1 self)
 Add to MetaCart
theoretical analysis. In this paper, we introduce a new multitask algorithm for a sequence of reinforcementlearning tasks when each task is sampled independently from (an unknown) distribution over a finite set of Markov decision processes whose parameters are initially unknown. For this setting, we prove
Reinforcement Learning I: Introduction
, 1998
"... In which we try to give a basic intuitive sense of what reinforcement learning is and how it differs and relates to other fields, e.g., supervised learning and neural networks, genetic algorithms and artificial life, control theory. Intuitively, RL is trial and error (variation and selection, search ..."
Abstract

Cited by 5500 (120 self)
 Add to MetaCart
In which we try to give a basic intuitive sense of what reinforcement learning is and how it differs and relates to other fields, e.g., supervised learning and neural networks, genetic algorithms and artificial life, control theory. Intuitively, RL is trial and error (variation and selection
for multitask reinforcement learning
"... Learning potential functions and their representations for multitask reinforcement learning ..."
Abstract
 Add to MetaCart
Learning potential functions and their representations for multitask reinforcement learning
Predicting How People Play Games: Reinforcement Learning . . .
 AMERICAN ECONOMIC REVIEW
, 1998
"... ..."
Bayesian MultiTask Reinforcement Learning
"... We consider the problem of multitask reinforcement learning where the learner is provided with a set of tasks, for which only a smallnumber ofsamplescanbe generatedfor any given policy. As the number of samples may not be enough to learn an accurate evaluation of the policy, it would be necessary t ..."
Abstract

Cited by 16 (0 self)
 Add to MetaCart
We consider the problem of multitask reinforcement learning where the learner is provided with a set of tasks, for which only a smallnumber ofsamplescanbe generatedfor any given policy. As the number of samples may not be enough to learn an accurate evaluation of the policy, it would be necessary
Locally weighted learning
 ARTIFICIAL INTELLIGENCE REVIEW
, 1997
"... This paper surveys locally weighted learning, a form of lazy learning and memorybased learning, and focuses on locally weighted linear regression. The survey discusses distance functions, smoothing parameters, weighting functions, local model structures, regularization of the estimates and bias, ass ..."
Abstract

Cited by 594 (53 self)
 Add to MetaCart
This paper surveys locally weighted learning, a form of lazy learning and memorybased learning, and focuses on locally weighted linear regression. The survey discusses distance functions, smoothing parameters, weighting functions, local model structures, regularization of the estimates and bias
SemiSupervised Learning Literature Survey
, 2006
"... We review the literature on semisupervised learning, which is an area in machine learning and more generally, artificial intelligence. There has been a whole
spectrum of interesting ideas on how to learn from both labeled and unlabeled data, i.e. semisupervised learning. This document is a chapter ..."
Abstract

Cited by 757 (8 self)
 Add to MetaCart
We review the literature on semisupervised learning, which is an area in machine learning and more generally, artificial intelligence. There has been a whole
spectrum of interesting ideas on how to learn from both labeled and unlabeled data, i.e. semisupervised learning. This document is a
Machine Learning in Automated Text Categorization
 ACM COMPUTING SURVEYS
, 2002
"... The automated categorization (or classification) of texts into predefined categories has witnessed a booming interest in the last ten years, due to the increased availability of documents in digital form and the ensuing need to organize them. In the research community the dominant approach to this p ..."
Abstract

Cited by 1658 (22 self)
 Add to MetaCart
to this problem is based on machine learning techniques: a general inductive process automatically builds a classifier by learning, from a set of preclassified documents, the characteristics of the categories. The advantages of this approach over the knowledge engineering approach (consisting in the manual
Bayesian MultiTask Reinforcement Learning
"... We consider the problem of multitask reinforcement learning where the learner is provided with a set of tasks, for which only a small number of samples can be generated for any given policy. As the number of samples may not be enough to learn an accurate evaluation of the policy, it would be necess ..."
Abstract
 Add to MetaCart
We consider the problem of multitask reinforcement learning where the learner is provided with a set of tasks, for which only a small number of samples can be generated for any given policy. As the number of samples may not be enough to learn an accurate evaluation of the policy, it would
Hierarchical Reinforcement Learning with the MAXQ Value Function Decomposition
 Journal of Artificial Intelligence Research
, 2000
"... This paper presents a new approach to hierarchical reinforcement learning based on decomposing the target Markov decision process (MDP) into a hierarchy of smaller MDPs and decomposing the value function of the target MDP into an additive combination of the value functions of the smaller MDPs. Th ..."
Abstract

Cited by 439 (6 self)
 Add to MetaCart
This paper presents a new approach to hierarchical reinforcement learning based on decomposing the target Markov decision process (MDP) into a hierarchy of smaller MDPs and decomposing the value function of the target MDP into an additive combination of the value functions of the smaller MDPs
Results 1  10
of
1,216,758