Julien Pérolat

1.4k total citations
13 papers, 120 citations indexed

About

Julien Pérolat is a scholar working on Artificial Intelligence, Management Science and Operations Research and Safety Research. According to data from OpenAlex, Julien Pérolat has authored 13 papers receiving a total of 120 indexed citations (citations by other indexed papers that have themselves been cited), including 10 papers in Artificial Intelligence, 8 papers in Management Science and Operations Research and 5 papers in Safety Research. Recurrent topics in Julien Pérolat's work include Reinforcement Learning in Robotics (7 papers), Experimental Behavioral Economics Studies (5 papers) and Advanced Bandit Algorithms Research (4 papers). Julien Pérolat is often cited by papers focused on Reinforcement Learning in Robotics (7 papers), Experimental Behavioral Economics Studies (5 papers) and Advanced Bandit Algorithms Research (4 papers). Julien Pérolat collaborates with scholars based in United Kingdom, United States and Singapore. Julien Pérolat's co-authors include Karl Tuyls, Rémi Munos, Marc Lanctot, Vinícius Zambaldi, Shayegan Omidshafiei, Thore Graepel, Mark Rowland, Sriram Srinivasan, Georgios Piliouras and Jean-Baptiste Lespiau and has published in prestigious journals such as Scientific Reports, Journal of Artificial Intelligence Research and Autonomous Agents and Multi-Agent Systems.

In The Last Decade

Julien Pérolat

13 papers receiving 109 citations

Peers

Julien Pérolat

AI

MSOR

SPS

EE

SR

Laurent Orseau United States

Mesrob I. Ohannessian United States

Erik Talvitie United States

John Aslanides United Kingdom

Masoud Mansoury Netherlands

Shahab Asoodeh Canada

Abdallah Saffidine France

Mohammed Albared Malaysia

Heinrich Jiang United States

Ashudeep Singh United States

Laurent Orseau United States

Julien Pérolat

74 ×1.0

AI

22 ×0.5

MSOR

7 ×0.3

SPS

4 ×0.2

EE

11 ×0.5

SR

Citations per year, relative to Julien Pérolat Julien Pérolat (= 1×) peers Laurent Orseau

Countries citing papers authored by Julien Pérolat

Since Specialization

Citations

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

Fields of papers citing papers by Julien Pérolat

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Julien Pérolat

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

All Works

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13 of 13 papers shown

1.

Perrin, Sarah, Mathieu Laurière, Julien Pérolat, et al.. (2022). Generalization in Mean Field Games by Learning Master Policies. Proceedings of the AAAI Conference on Artificial Intelligence. 36(9). 9413–9421. 5 indexed citations

2.

Fu, Justin, Andrea Tacchetti, Julien Pérolat, & Yoram Bachrach. (2021). Evaluating Strategic Structures in Multi-Agent Inverse Reinforcement Learning. Journal of Artificial Intelligence Research. 71. 925–951. 7 indexed citations

3.

Munos, Rémi, Julien Pérolat, Jean-Baptiste Lespiau, et al.. (2020). Fast computation of Nash Equilibria in Imperfect Information Games. International Conference on Machine Learning. 1. 7119–7129. 1 indexed citations

4.

Hennes, Daniel, Dustin Morrill, Shayegan Omidshafiei, et al.. (2020). Neural Replicator Dynamics: Multiagent Learning via Hedging Policy Gradients. 492–501. 8 indexed citations

5.

Omidshafiei, Shayegan, Karl Tuyls, Wojciech Marian Czarnecki, et al.. (2020). Navigating the Landscape of Games.. arXiv (Cornell University). 2 indexed citations

6.

Leibo, Joel Z., Julien Pérolat, Edward Hughes, et al.. (2019). Malthusian Reinforcement Learning. arXiv (Cornell University). 1099–1107. 5 indexed citations

7.

Élie, Romuald, Julien Pérolat, Mathieu Laurière, Matthieu Geist, & Olivier Pietquin. (2019). Approximate Fictitious Play for Mean Field Games. arXiv (Cornell University). 5 indexed citations

8.

Omidshafiei, Shayegan, Christos H. Papadimitriou, Georgios Piliouras, et al.. (2019). α-Rank: Multi-Agent Evaluation by Evolution. Scientific Reports. 9(1). 9937–9937. 27 indexed citations

9.

Rowland, Mark, Shayegan Omidshafiei, Karl Tuyls, et al.. (2019). Multiagent Evaluation under Incomplete Information. arXiv (Cornell University). 32. 12270–12282. 8 indexed citations

10.

Tuyls, Karl, Julien Pérolat, Marc Lanctot, et al.. (2019). Bounds and dynamics for empirical game theoretic analysis. Autonomous Agents and Multi-Agent Systems. 34(1). 5 indexed citations

11.

Balduzzi, David, Karl Tuyls, Julien Pérolat, & Thore Graepel. (2018). Re-evaluating evaluation. UCL Discovery (University College London). 31. 3268–3279. 3 indexed citations

12.

Srinivasan, Sriram, Marc Lanctot, Vinícius Zambaldi, et al.. (2018). Actor-Critic Policy Optimization in Partially Observable Multiagent Environments. arXiv (Cornell University). 31. 3422–3435. 29 indexed citations

13.

Pérolat, Julien, Joel Z. Leibo, Vinícius Zambaldi, et al.. (2017). A multi-agent reinforcement learning model of common-pool resource appropriation. UCL Discovery (University College London). 30. 3643–3652. 15 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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