Self-supervised Reinforcement Learning
Self-Supervised Reinforcement Learning is a reinforcement learning paradigm that falls under the broader category of self-supervised learning methods, distinguished by its use of autonomously generated supervision signals from the agent's environment interactions to guide representation learning and policy optimization without reliance on externally annotated data.
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2017–2026
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Curiosity-Driven Exploration by Self-Supervised Prediction
Deepak Pathak, Pulkit Agrawal, Alexei A. Efros et al. · 2017 · 688 citations · Full text
Emergent Tool Use From Multi-Agent Autocurricula
Bowen Baker, Ingmar Kanitscheider, Todor Markov et al. · arXiv (Cornell University) · 2019 · 337 citations · Full text
Self-Supervised Reinforcement Learning for Recommender Systems
Xin Xin, Alexandros Karatzoglou, Ioannis Arapakis et al. · 2020 · 213 citations · Full text
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University of California, Berkeley
Berkeley, United States
9 h-index
32 publications
4K citations
Google (United States)
Mountain View, United States
6 h-index
15 publications
1.1K citations
Nanyang Technological University
Singapore, Singapore
4 h-index
7 publications
270 citations

University of Glasgow
Glasgow, United Kingdom
3 h-index
5 publications
509 citations
New York University
New York, United States
3 h-index
5 publications
122 citations

Chinese Academy of Sciences
Beijing, China
3 h-index
10 publications
246 citations
Google (United Kingdom)
London, United Kingdom
3 h-index
3 publications
279 citations
Meta (Israel)
Tel Aviv, Israel
3 h-index
7 publications
258 citations
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Repository · 29 publications · 2.2K citations
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4 publications · 76 citations
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