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.
Trust Region Policy Optimization
20151.6k citationsJohn Schulman, Sergey Levine et al.arXiv (Cornell University)profile →
Motion planning with sequential convex optimization and convex collision checking
2014539 citationsJohn Schulman, Yan Duan et al.The International Journal of Robotics Researchprofile →
Peers — A (Enhanced Table)
Peers by citation overlap · career bar shows stage (early→late)
cites ·
hero ref
This map shows the geographic impact of John Schulman'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 John Schulman with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites John Schulman more than expected).
This network shows the impact of papers produced by John Schulman. 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 John Schulman. The network helps show where John Schulman may publish in the future.
Co-authorship network of co-authors of John Schulman
This figure shows the co-authorship network connecting the top 25 collaborators of John Schulman.
A scholar is included among the top collaborators of John Schulman 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 John Schulman. John Schulman is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Jun, Heewoo, Rewon Child, Mark Chen, et al.. (2020). Distribution Augmentation for Generative Modeling. International Conference on Machine Learning. 5006–5019.8 indexed citations
3.
Cobbe, Karl, et al.. (2020). Leveraging Procedural Generation to Benchmark Reinforcement Learning. International Conference on Machine Learning. 1. 2048–2056.8 indexed citations
Houthooft, Rein, Xi Chen, Yan Duan, et al.. (2016). VIME: Variational Information Maximizing Exploration. Ghent University Academic Bibliography (Ghent University). 29. 1109–1117.55 indexed citations
Chen, Xi, Diederik P. Kingma, Tim Salimans, et al.. (2016). Variational Lossy Autoencoder. arXiv (Cornell University).89 indexed citations
8.
Duan, Yan, Xi Chen, Rein Houthooft, John Schulman, & Pieter Abbeel. (2016). Benchmarking deep reinforcement learning for continuous control. 1329–1338.163 indexed citations
9.
Schulman, John. (2016). Optimizing Expectations: From Deep Reinforcement Learning to Stochastic Computation Graphs. eScholarship (California Digital Library).21 indexed citations
10.
Schulman, John, Sergey Levine, Philipp Moritz, Michael I. Jordan, & Pieter Abbeel. (2015). Trust Region Policy Optimization. arXiv (Cornell University). 1889–1897.1573 indexed citations breakdown →
Schulman, John, Yan Duan, Jonathan Ho, et al.. (2014). Motion planning with sequential convex optimization and convex collision checking. The International Journal of Robotics Research. 33(9). 1251–1270.539 indexed citations breakdown →
Merrill, D.W., et al.. (1987). ILLUSTRATIONS OF A DENSITY-EQUALIZING MAP PROJECTION TECHNIQUE. eScholarship (California Digital Library).1 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.