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.
Dueling Network Architectures for Deep Reinforcement Learning
20151.0k citationsTom Schaul, Matteo Hessel et al.arXiv (Cornell University)profile →
Rainbow: Combining Improvements in Deep Reinforcement Learning
2018995 citationsMatteo Hessel, Joseph Modayil et al.profile →
Deep Q-learning From Demonstrations
2018485 citationsTodd Hester, Olivier Pietquin et al.profile →
This map shows the geographic impact of Tom Schaul'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 Tom Schaul with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Tom Schaul more than expected).
This network shows the impact of papers produced by Tom Schaul. 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 Tom Schaul. The network helps show where Tom Schaul may publish in the future.
Co-authorship network of co-authors of Tom Schaul
This figure shows the co-authorship network connecting the top 25 collaborators of Tom Schaul.
A scholar is included among the top collaborators of Tom Schaul 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 Tom Schaul. Tom Schaul is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Ashlock, Dan, et al.. (2019). Representation in Evolutionary Computation for Games. Data Archiving and Networked Services (DANS). 9(12).1 indexed citations
4.
Hester, Todd, Olivier Pietquin, Marc Lanctot, et al.. (2017). Learning from Demonstrations for Real World Reinforcement Learning. arXiv (Cornell University).43 indexed citations
5.
Silver, David, Hado van Hasselt, Matteo Hessel, et al.. (2017). The predictron: end-to-end learning and planning. International Conference on Machine Learning. 3191–3199.25 indexed citations
6.
Xu, Zhongwen, Joseph Modayil, Hado P. van Hasselt, et al.. (2017). Natural Value Approximators: Learning when to Trust Past Estimates. Neural Information Processing Systems. 30. 2120–2128.
Schaul, Tom. (2011). Studies in Continuous Black-box Optimization. mediaTUM – the media and publications repository of the Technical University Munich (Technical University Munich).6 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.