Entropy-based Exploration
Entropy-Based Exploration is a reinforcement learning exploration strategy that uses the entropy of the policy distribution as a measure of uncertainty to encourage the agent to visit states and take actions that maximize information gain and reduce predictive uncertainty.
29
Publications
846
Citations
114
Authors
55
Institutions
Publications per year
2017–2026
17
29
Provably Efficient Maximum Entropy Exploration
Elad Hazan, Sham M. Kakade, Karan Singh et al. · arXiv (Cornell University) · 2018 · 96 citations · Full text
Exploration of gene–gene interaction effects using entropy-based methods
Changzheng Dong, Xun Chu, Ying Wang et al. · European Journal of Human Genetics · 2007 · 81 citations · Full text
A kriging and entropy-based approach to raingauge network design
Pengcheng Xu, Dong Wang, Vijay P. Singh et al. · Environmental Research · 2017 · 45 citations
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55

Xi'an Jiaotong University
Xi'an, China
2 h-index
5 publications
60 citations
University of Stuttgart
Stuttgart, Germany
2 h-index
6 publications
99 citations
Simon Fraser University
Burnaby, Canada
2 h-index
5 publications
60 citations
Nanjing University
Nanjing, China
2 h-index
10 publications
370 citations
Technische Universität Berlin
Berlin, Germany
2 h-index
5 publications
78 citations
Google (United States)
Mountain View, United States
1 h-index
3 publications
132 citations
University of Oxford
Oxford, United Kingdom
1 h-index
1 publications
15 citations
Institute for Systems Engineering and Computers
Lisbon, Portugal
1 h-index
2 publications
34 citations
1–8 of 55
Repository · 6 publications · 228 citations
6
h-index
1 publications · 12 citations
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1 publications · 11 citations
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1 publications · 45 citations
1
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1 publications · 10 citations
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