Peter Sunehag

1.6k citations
26 papers · 346 · h-index 7

Impact in

Papers in

Peter Sunehag

22 papers receiving 328 citations

Peers

Peter Sunehag
Comparison fields: 5 of 57
  • Artificial Intelligence 224
  • Computational Theory and Mathematics 63
  • Computer Networks and Communications 87
  • Management Science and Operations Research 28
  • Computer Vision and Pattern Recognition 44
Replace Kun Shao with:
Kun Shao China
Landon Kraemer United States
Abdel‐Illah Mouaddib France
Shayegan Omidshafiei United States
Richard Valenzano Canada
Alessandro Lazaric Italy
Charles Gretton Australia
Ruilin Liu United States
Antonín Komenda Czechia
Marie Farrell United Kingdom
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Citations per year

Countries citing papers authored by Peter Sunehag

Since Specialization
Citations

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

Fields of papers citing papers by Peter Sunehag

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 25 scholars most cited alongside Peter Sunehag, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Peter Sunehag Line = papers co-authored together Peter Sunehag links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 26 papers — load more, or switch the sort, to bring in the rest.

#Work
1 2018240
2 201024
3 200911
4 20239
5
Q-learning for history-based reinforcement learning
20137
6
Rationality, optimism and guarantees in general reinforcement learning
20156
7 20196
8 20195
9 20125
10 20085
11 20194
12
Context tree maximizing reinforcement learning
20124
13
Feature Reinforcement Learning: State of the Art
20143
14 20132
15 20042
16 20032
17 20072
18
Feature Reinforcement Learning using Looping Suffix Trees
20122
19
Interpolation of Subcouples, New Results and Applications
20032
20
Emerge and spread models and word burstiness
20071

About Peter Sunehag

Peter Sunehag is a scholar working on Artificial Intelligence, Management Science and Operations Research, Signal Processing, Applied Mathematics and Computer Vision and Pattern Recognition, having authored 26 papers that have together received 346 indexed citations. Recurring topics across this work include Reinforcement Learning in Robotics (10 papers), Advanced Harmonic Analysis Research (4 papers), Artificial Intelligence in Games (3 papers), Algorithms and Data Compression (3 papers), Anomaly Detection Techniques and Applications (3 papers), Evolutionary Algorithms and Applications (3 papers), Holomorphic and Operator Theory (3 papers) and Time Series Analysis and Forecasting (3 papers). The work is most often cited by research in Artificial Intelligence (224 citations), Computational Theory and Mathematics (63 citations), Computer Networks and Communications (87 citations), Management Science and Operations Research (28 citations) and Computer Vision and Pattern Recognition (44 citations). Peter Sunehag has collaborated with scholars based in Australia, United Kingdom and Sweden. Frequent co-authors include Joel Z. Leibo, Guy Lever, Thore Graepel, Audrūnas Gruslys, Max Jaderberg, Marc Lanctot, Nicolas Sonnerat, Karl Tuyls, Vinícius Zambaldi and Wojciech Marian Czarnecki. Their work appears in journals such as Journal of Machine Learning Research, Pervasive and Mobile Computing, Journal of Mathematical Analysis and Applications, Behavioral and Brain Sciences and Studia Mathematica.

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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