Journal ArticleDOI
A survey of robot learning from demonstration
TLDR
A comprehensive survey of robot Learning from Demonstration (LfD), a technique that develops policies from example state to action mappings, which analyzes and categorizes the multiple ways in which examples are gathered, as well as the various techniques for policy derivation.About:
This article is published in Robotics and Autonomous Systems.The article was published on 2009-05-01. It has received 3343 citations till now. The article focuses on the topics: Robot learning & Programming by demonstration.read more
Citations
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Journal ArticleDOI
Reinforcement learning in robotics: A survey
TL;DR: This article attempts to strengthen the links between the two research communities by providing a survey of work in reinforcement learning for behavior generation in robots by highlighting both key challenges in robot reinforcement learning as well as notable successes.
Reinforcement Learning in Robotics: A Survey.
Jens Kober,Jan Peters +1 more
TL;DR: A survey of work in reinforcement learning for behavior generation in robots can be found in this article, where the authors highlight key challenges in robot reinforcement learning as well as notable successes and discuss the role of algorithms, representations and prior knowledge in achieving these successes.
Proceedings Article
A Reduction of Imitation Learning and Structured Prediction to No-Regret Online Learning
TL;DR: In this article, a no-regret algorithm is proposed to find a policy with good performance under the distribution of observations it induces in such sequential settings, which can be seen as a no regret algorithm in an online learning setting.
Posted Content
A Reduction of Imitation Learning and Structured Prediction to No-Regret Online Learning
TL;DR: In this article, a no-regret algorithm is proposed to train a stationary deterministic policy with good performance under the distribution of observations it induces in such sequential settings, and it outperforms previous approaches on two challenging imitation learning problems and a benchmark sequence labeling problem.
Journal Article
A comprehensive survey on safe reinforcement learning
Javier García,Fernando Fernández +1 more
TL;DR: This work categorize and analyze two approaches of Safe Reinforcement Learning, based on the modification of the optimality criterion, the classic discounted finite/infinite horizon, with a safety factor and the incorporation of external knowledge or the guidance of a risk metric.
References
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Book
Reinforcement Learning: An Introduction
TL;DR: This book provides a clear and simple account of the key ideas and algorithms of reinforcement learning, which ranges from the history of the field's intellectual foundations to the most recent developments and applications.
Book
The Elements of Statistical Learning: Data Mining, Inference, and Prediction
TL;DR: In this paper, the authors describe the important ideas in these areas in a common conceptual framework, and the emphasis is on concepts rather than mathematics, with a liberal use of color graphics.
Journal ArticleDOI
An introduction to variable and feature selection
Isabelle Guyon,André Elisseeff +1 more
TL;DR: The contributions of this special issue cover a wide range of aspects of variable selection: providing a better definition of the objective function, feature construction, feature ranking, multivariate feature selection, efficient search methods, and feature validity assessment methods.
Journal ArticleDOI
The Elements of Statistical Learning: Data Mining, Inference, and Prediction
TL;DR: The Elements of Statistical Learning: Data Mining, Inference, and Prediction as discussed by the authors is a popular book for data mining and machine learning, focusing on data mining, inference, and prediction.
Proceedings ArticleDOI
Apprenticeship learning via inverse reinforcement learning
Pieter Abbeel,Andrew Y. Ng +1 more
TL;DR: This work thinks of the expert as trying to maximize a reward function that is expressible as a linear combination of known features, and gives an algorithm for learning the task demonstrated by the expert, based on using "inverse reinforcement learning" to try to recover the unknown reward function.