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Richard Tanburn

dblp:167/4107 · DBLP profile ↗
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1ranked-venue papers
0as first author
0since 2021 · last 2020
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Artificial intelligence and machine learning · 1

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Artificial intelligence
1 paper
Reinforcement learning · 67% Robot manipulation · 33%

Topics — the 3 heaviest of 3, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Machine learning › Reinforcement learning
exploration		 0.412020
Making Efficient Use of Demonstrations to Solve Hard Exploration Problems · ICLR 2020
Machine learning › Reinforcement learning › exploration › exploration in markov decision processes
hard exploration		 0.412020
Making Efficient Use of Demonstrations to Solve Hard Exploration Problems · ICLR 2020
Robotics › Robot manipulation
learning from demonstration		 0.412020
Making Efficient Use of Demonstrations to Solve Hard Exploration Problems · ICLR 2020

Methods — techniques the papers use, named apart from their topics

demonstration-based learning · 0.4
YearPublicationVenuePosition
2020 Making Efficient Use of Demonstrations to Solve Hard Exploration Problems
Caglar Gulcehre, Tom Le Paine, Bobak Shahriari, Misha Denil, Matt Hoffman 0001, Hubert Soyer, Richard Tanburn, Steven Kapturowski, Neil C. Rabinowitz, Duncan Williams, Gabriel Barth-Maron, Ziyu Wang 0001, Nando de Freitas
 ICLR7