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.
Counterfactual Multi-Agent Policy Gradients
20181.0k citationsJakob Foerster, Gregory Farquhar et al.Proceedings of the AAAI Conference on Artificial Intelligenceprofile →
Peers — A (Enhanced Table)
Peers by citation overlap · career bar shows stage (early→late)
cites ·
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Countries citing papers authored by Gregory Farquhar
Since
Specialization
Citations
This map shows the geographic impact of Gregory Farquhar'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 Gregory Farquhar with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Gregory Farquhar more than expected).
Fields of papers citing papers by Gregory Farquhar
This network shows the impact of papers produced by Gregory Farquhar. 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 Gregory Farquhar. The network helps show where Gregory Farquhar may publish in the future.
Co-authorship network of co-authors of Gregory Farquhar
This figure shows the co-authorship network connecting the top 25 collaborators of Gregory Farquhar.
A scholar is included among the top collaborators of Gregory Farquhar 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 Gregory Farquhar. Gregory Farquhar is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
All Works
9 of 9 papers shown
1.
Rashid, Tabish, Gregory Farquhar, Bei Peng, & Shimon Whiteson. (2020). Weighted QMIX: Expanding Monotonic Value Function Factorisation.. arXiv (Cornell University).8 indexed citations
Samvelyan, Mikayel, Tabish Rashid, Christian Schroeder de Witt, et al.. (2019). The StarCraft Multi-Agent Challenge. arXiv (Cornell University). 2186–2188.5 indexed citations
4.
Farquhar, Gregory, Shimon Whiteson, & Jakob Foerster. (2019). Loaded DiCE: Trading off Bias and Variance in Any-Order Score Function Gradient Estimators for Reinforcement Learning. Oxford University Research Archive (ORA) (University of Oxford). 32. 8149–8160.
5.
Foerster, Jakob, et al.. (2019). A Baseline for Any Order Gradient Estimation in Stochastic Computation Graphs. Oxford University Research Archive (ORA) (University of Oxford). 4343–4351.1 indexed citations
6.
Foerster, Jakob, Gregory Farquhar, Triantafyllos Afouras, Nantas Nardelli, & Shimon Whiteson. (2018). Counterfactual Multi-Agent Policy Gradients. Proceedings of the AAAI Conference on Artificial Intelligence. 32(1).1011 indexed citations breakdown →
7.
Farquhar, Gregory, Tim Rocktäschel, Maximilian Igl, & Shimon Whiteson. (2017). TreeQN and ATreeC: Differentiable Tree Planning for Deep Reinforcement Learning. Oxford University Research Archive (ORA) (University of Oxford).4 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.