Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies
if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the
same subfield and year (this is the minimum needed to enter the top 1%, not the average
within it), or reaches the top citation threshold in at least one of its specific research
topics.
Deep Q-learning From Demonstrations
2018485 citationsTodd Hester, Olivier Pietquin et al.profile →
Challenges of real-world reinforcement learning: definitions, benchmarks and analysis
2021320 citationsGabriel Dulac-Arnold, Nir Levine et al.Machine Learningprofile →
Peers — A (Enhanced Table)
Peers by citation overlap · career bar shows stage (early→late)
cites ·
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This map shows the geographic impact of Todd Hester's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Todd Hester with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Todd Hester more than expected).
This network shows the impact of papers produced by Todd Hester. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Todd Hester. The network helps show where Todd Hester may publish in the future.
Co-authorship network of co-authors of Todd Hester
This figure shows the co-authorship network connecting the top 25 collaborators of Todd Hester.
A scholar is included among the top collaborators of Todd Hester based on the total number of
citations received by their joint publications. Widths of edges
represent the number of papers authors have co-authored together.
Node borders
signify the number of papers an author published with Todd Hester. Todd Hester is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
All Works
20 of 20 papers shown
1.
Dulac-Arnold, Gabriel, Nir Levine, Daniel J. Mankowitz, et al.. (2021). Challenges of real-world reinforcement learning: definitions, benchmarks and analysis. Machine Learning. 110(9). 2419–2468.320 indexed citations breakdown →
2.
Mankowitz, Daniel J., Nir Levine, Abbas Abdolmaleki, et al.. (2020). Robust Reinforcement Learning for Continuous Control with Model Misspecification. arXiv (Cornell University).1 indexed citations
3.
Hester, Todd, Olivier Pietquin, Marc Lanctot, et al.. (2017). Learning from Demonstrations for Real World Reinforcement Learning. arXiv (Cornell University).43 indexed citations
Konidaris, George, Byron Boots, Stephen Hart, et al.. (2012). Designing intelligent robots : reintegrating AI : papers from the AAAI Spring Symposium.4 indexed citations
7.
Hester, Todd & Peter Stone. (2012). TEXPLORE: Real-Time Sample-Efficient Reinforcement Learning for Robots. National Conference on Artificial Intelligence. 21–26.2 indexed citations
Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive
bibliographic database. While OpenAlex provides broad and valuable coverage of the global
research landscape, it—like all bibliographic datasets—has inherent limitations. These include
incomplete records, variations in author disambiguation, differences in journal indexing, and
delays in data updates. As a result, some metrics and network relationships displayed in
Rankless may not fully capture the entirety of a scholar's output or impact.