Guy Lever

3.8k citations
15 papers · 792 · 1 hit paper · h-index 9

Impact in

Papers in

Guy Lever

15 papers receiving 764 citations

Guy Lever's Hit Papers

Human-level performance in 3D multiplayer games with population-based reinforcement learning 2019 · 349 citations
3490+2+4Years since publication100200300

Peers

Guy Lever
Comparison fields: 5 of 83
  • Artificial Intelligence 571
  • Management Science and Operations Research 90
  • Computational Theory and Mathematics 107
  • Computer Networks and Communications 133
  • Computer Vision and Pattern Recognition 91
Replace Zhuoran Yang with:
Zhuoran Yang United States
Georgios Theocharous United States
Yifei Wang China
Youssef Drissi Belgium
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Nicolas Sonnerat United Kingdom
Youwen Zhu China
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Citations per field
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Citations per year

Countries citing papers authored by Guy Lever

Since Specialization
Citations

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

Fields of papers citing papers by Guy Lever

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 25 scholars most cited alongside Guy Lever, 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 Guy Lever Line = papers co-authored together Guy Lever links everyone, so they are left out of the graph.

All Works

15 of 15 papers shown
#Work
1
Human-level performance in 3D multiplayer games with population-based reinforcement learning
Hit paper breakdown →
2019349
2 2018229
3
Conditional mean embeddings as regressors
201241
4 201239
5
Modelling transition dynamics in MDPs with RKHS embeddings
201228
6
Predicting the Labelling of a Graph via Minimum $p$-Seminorm Interpolation.
200923
7
Modelling Policies in MDPs in Reproducing Kernel Hilbert Space
201519
8
Online Prediction on Large Diameter Graphs
200818
9 201911
10 20117
11 20196
12
Approximate Newton methods for policy search in Markov decision processes
20166
13 20196
14 20166
15 20194

About Guy Lever

Guy Lever is a scholar working on Artificial Intelligence, Computer Networks and Communications, Statistical and Nonlinear Physics, Computational Theory and Mathematics and Management Science and Operations Research, having authored 15 papers that have together received 792 indexed citations. Recurring topics across this work include Reinforcement Learning in Robotics (6 papers), Machine Learning and Algorithms (5 papers), Evolutionary Algorithms and Applications (3 papers), Markov Chains and Monte Carlo Methods (2 papers), Advanced Bandit Algorithms Research (2 papers), Complexity and Algorithms in Graphs (2 papers), Gaussian Processes and Bayesian Inference (2 papers) and Model Reduction and Neural Networks (2 papers). The work is most often cited by research in Artificial Intelligence (571 citations), Management Science and Operations Research (90 citations), Computational Theory and Mathematics (107 citations), Computer Networks and Communications (133 citations) and Computer Vision and Pattern Recognition (91 citations). Guy Lever has collaborated with scholars based in United Kingdom, Australia and Canada. Frequent co-authors include Thore Graepel, Joel Z. Leibo, Wojciech Marian Czarnecki, Max Jaderberg, Nicolas Sonnerat, Peter Sunehag, Karl Tuyls, Vinícius Zambaldi, Audrūnas Gruslys and Marc Lanctot. Their work appears in journals such as Theoretical Computer Science, Science, Journal of Machine Learning Research, UCL Discovery (University College London) and arXiv (Cornell University).

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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