Learning From Play
Learning From Play is a machine learning paradigm within the broader category of Learning From Demonstration that leverages unsupervised, self-generated interaction data from agent exploration—rather than explicit human demonstrations—to infer policies or skills through the identification of structured, reusable behavioral patterns.
188
Publications
14.3K
Citations
517
Authors
206
Institutions
Publications per year
2017–2026
67
Parents
188
DeepStack: Expert-level artificial intelligence in heads-up no-limit poker
Matej Moravčík, Martin Schmid, Neil Burch et al. · Science · 2017 · 812 citations · Full text
COMPUTING “ELO RATINGS” OF MOVE PATTERNS IN THE GAME OF GO1
Rémi Coulom · ICGA Journal · 2007 · 300 citations · Full text
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Georgia Institute of Technology
Atlanta, United States
8 h-index
23 publications
1.2K citations
Simon Fraser University
Burnaby, Canada
6 h-index
7 publications
231 citations
Carnegie Mellon University
Pittsburgh, United States
5 h-index
8 publications
417 citations

University of Toronto
Toronto, Canada
4 h-index
5 publications
205 citations
University of Illinois Urbana-Champaign
Urbana, United States
4 h-index
5 publications
300 citations

University of Edinburgh
Edinburgh, United Kingdom
4 h-index
6 publications
250 citations
Google DeepMind (United Kingdom)
London, United Kingdom
3 h-index
7 publications
600 citations

University of Groningen
Groningen, The Netherlands
3 h-index
4 publications
163 citations
1–8 of 206
Repository · 18 publications · 806 citations
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5 publications · 150 citations
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5 publications · 349 citations
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JICRCR · 2 publications · 85 citations
3
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Conference · 2 publications · 78 citations
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