Exploration Bonuses
Exploration Bonuses is a reinforcement learning strategy that augments estimated action values with an additional term incentivizing the selection of less-visited states or actions, thereby promoting systematic exploration in sequential decision-making environments.
28
Publications
3.5K
Citations
91
Authors
37
Institutions
Publications per year
2017–2026
18
Variants
Exploration Bonus
28
The anatomy of choice: active inference and agency
Karl Friston, Philipp Schwartenbeck, Thomas H. B. FitzGerald et al. · Frontiers in Human Neuroscience · 2013 · 347 citations · Full text
Business, Economics, Psychology +17
Exploration by Random Network Distillation
Yuri Burda, Harrison Edwards, Amos Storkey et al. · arXiv (Cornell University) · 2018 · 259 citations · Full text
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37
Google (United States)
Mountain View, United States
6 h-index
18 publications
1.1K citations
Google DeepMind (United Kingdom)
London, United Kingdom
3 h-index
13 publications
5.6K citations
University College London
London, United Kingdom
3 h-index
11 publications
3.1K citations
Wellcome Centre for Human Neuroimaging
London, United Kingdom
2 h-index
5 publications
1.2K citations
OpenAI (United States)
San Francisco, United States
2 h-index
5 publications
1.5K citations
Meta (Israel)
Tel Aviv, Israel
2 h-index
3 publications
78 citations
University of California, Berkeley
Berkeley, United States
2 h-index
7 publications
2.1K citations
Institute of Cognitive and Brain Sciences
New York, United States
2 h-index
2 publications
165 citations
1–8 of 37
Repository · 11 publications · 1.5K citations
11
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2 publications · 165 citations
2
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Conference · 2 publications · 37 citations
2
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Conference · 2 publications · 701 citations
2
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Conference · 2 publications · 189 citations
2
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