Nantas Nardelli

3.3k total citations · 1 hit paper
6 papers, 1.2k citations indexed

About

Nantas Nardelli is a scholar working on Artificial Intelligence, Electrical and Electronic Engineering and Automotive Engineering. According to data from OpenAlex, Nantas Nardelli has authored 6 papers receiving a total of 1.2k indexed citations (citations by other indexed papers that have themselves been cited), including 6 papers in Artificial Intelligence, 2 papers in Electrical and Electronic Engineering and 1 paper in Automotive Engineering. Recurrent topics in Nantas Nardelli's work include Reinforcement Learning in Robotics (6 papers), Fuel Cells and Related Materials (2 papers) and Autonomous Vehicle Technology and Safety (1 paper). Nantas Nardelli is often cited by papers focused on Reinforcement Learning in Robotics (6 papers), Fuel Cells and Related Materials (2 papers) and Autonomous Vehicle Technology and Safety (1 paper). Nantas Nardelli collaborates with scholars based in United Kingdom, Israel and United States. Nantas Nardelli's co-authors include Gregory Farquhar, Jakob Foerster, Shimon Whiteson, Triantafyllos Afouras, Subramanian Ramamoorthy, Gabriel Synnaeve, Zeming Lin, Tabish Rashid, Philip H. S. Torr and Nicolas Usunier and has published in prestigious journals such as Oxford University Research Archive (ORA) (University of Oxford), arXiv (Cornell University) and UCL Discovery (University College London).

In The Last Decade

Nantas Nardelli

6 papers receiving 1.2k citations

Hit Papers

align trajectories

What are hit papers?

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

2018 1.0k citations Jakob Foerster, Gregory Farquhar et al. Proceedings of the AAAI Conference on Artificial Intelligence profile →

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name	h						Papers	Cites
Nantas Nardelli United Kingdom	5	746	320	211	162	154	6	1.2k
Gregory Farquhar United Kingdom	6	749 1.0×	320 1.0×	208 1.0×	165 1.0×	149 1.0×	9	1.2k
Frans A. Oliehoek Netherlands	16	755 1.0×	286 0.9×	154 0.7×	105 0.6×	137 0.9×	69	1.2k
Ian Osband United States	12	875 1.2×	253 0.8×	266 1.3×	234 1.4×	188 1.2×	20	1.4k
Gabriel Dulac-Arnold United Kingdom	6	594 0.8×	143 0.4×	308 1.5×	131 0.8×	158 1.0×	8	1.0k
Kagan Tumer United States	17	473 0.6×	183 0.6×	123 0.6×	70 0.4×	140 0.9×	45	877
Adi Botea Ireland	19	758 1.0×	359 1.1×	149 0.7×	67 0.4×	612 4.0×	79	1.3k
Dayong Ye Australia	20	543 0.7×	560 1.8×	151 0.7×	238 1.5×	93 0.6×	75	1.3k
I. Grondman Netherlands	6	349 0.5×	170 0.5×	269 1.3×	203 1.3×	74 0.5×	8	841
Chou‐Yuan Lee Taiwan	13	362 0.5×	228 0.7×	230 1.1×	69 0.4×	75 0.5×	32	973
Martijn van Otterlo Netherlands	11	499 0.7×	118 0.4×	164 0.8×	93 0.6×	137 0.9×	34	899

Countries citing papers authored by Nantas Nardelli

Since Specialization

Citations

This map shows the geographic impact of Nantas Nardelli'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 Nantas Nardelli with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Nantas Nardelli more than expected).

Fields of papers citing papers by Nantas Nardelli

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Nantas Nardelli. 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 Nantas Nardelli. The network helps show where Nantas Nardelli may publish in the future.

Co-authorship network of co-authors of Nantas Nardelli

This figure shows the co-authorship network connecting the top 25 collaborators of Nantas Nardelli. A scholar is included among the top collaborators of Nantas Nardelli 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 Nantas Nardelli. Nantas Nardelli is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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6 of 6 papers shown

1.

Küttler, Heinrich, Nantas Nardelli, Alexander Miller, et al.. (2020). The NetHack Learning Environment. UCL Discovery (University College London). 33. 7671–7684. 2 indexed citations

2.

Samvelyan, Mikayel, Tabish Rashid, Christian Schroeder de Witt, et al.. (2019). The StarCraft Multi-Agent Challenge. arXiv (Cornell University). 2186–2188. 5 indexed citations

3.

Nardelli, Nantas, Gabriel Synnaeve, Zeming Lin, et al.. (2018). Value Propagation Networks.. Oxford University Research Archive (ORA) (University of Oxford). 6 indexed citations

4.

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 →

5.

Foerster, Jakob, Gregory Farquhar, Triantafyllos Afouras, Nantas Nardelli, & Shimon Whiteson. (2017). Counterfactual Multi-Agent Policy Gradients. arXiv (Cornell University). 32(1). 2974–2982. 171 indexed citations

6.

Nardelli, Nantas, et al.. (2015). Counterfactual reasoning about intent for interactive navigation in dynamic environments. arXiv (Cornell University). 11 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.

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