Multi-step Exploration
Multi-Step Exploration is a sequential decision-making strategy within reinforcement learning that systematically extends exploration over multiple time steps to balance the acquisition of new information with the refinement of policy performance, distinguishing itself from single-step methods by considering the long-term impact of exploratory actions.
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2017–2026
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Supporting Complex Search Tasks
Ahmed Hassan Awadallah, Ryen W. White, Patrick Pantel et al. · 2014 · 91 citations
The Continuous Hint Factory - Providing Hints in Vast and Sparsely Populated Edit Distance Spaces
Barbara Hammer, Thomas Price, Tiffany Barnes et al. · Zenodo (CERN European Organization for Nuclear Research) · 2018 · 31 citations · Full text
Education, Educational Technology, Artificial Intelligence In Education +13
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University of California, Berkeley
Berkeley, United States
2 h-index
4 publications
88 citations

Université Laval
Québec, Canada
1 h-index
4 publications
40 citations
Délégation Paris 6
Paris, France
1 h-index
2 publications
20 citations

Sorbonne Université
Paris, France
1 h-index
2 publications
20 citations
Shandong Institute of Automation
Jinan, China
1 h-index
2 publications
44 citations
Beijing Academy of Artificial Intelligence
Beijing, China
1 h-index
4 publications
88 citations
Massachusetts Institute of Technology
Cambridge, United States
1 h-index
1 publications
19 citations

Johns Hopkins University
Baltimore, United States
1 h-index
1 publications
51 citations
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Repository · 2 publications · 63 citations
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1 publications · 32 citations
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Repository · 1 publications · 31 citations
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1 publications · 55 citations
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Repository · 1 publications · 12 citations
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