Raphaël Fonteneau

46 papers receiving 593 citations

Peers

Raphaël Fonteneau
Comparison fields: 5 of 86
  • Energy Engineering and Power Technology 52
  • Control and Systems Engineering 200
  • Electrical and Electronic Engineering 395
  • Virology 24
  • Artificial Intelligence 98
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Countries citing papers authored by Raphaël Fonteneau

Since Specialization
Citations

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

Fields of papers citing papers by Raphaël Fonteneau

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

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

All Works

20 of 20 papers shown

Showing the 20 most-cited of 49 papers — load more, or switch the sort, to bring in the rest.

#Work
1 2017149
2
Deep Reinforcement Learning Solutions for Energy Microgrids Management
201698
3 201844
4 202037
5 201424
6 201223
7 201920
8 202119
9
Learning Exploration/Exploitation Strategies for Single Trajectory Reinforcement Learning
201214
10 202014
11 201513
12
Foreseeing New Control Challenges in Electricity Prosumer Communities
201712
13 200812
14
Variable selection for dynamic treatment regimes: a reinforcement learning approach
200811
15 201710
16 200910
17 20129
18 20099
19 20168
20 20178

About Raphaël Fonteneau

Raphaël Fonteneau is a scholar working on Artificial Intelligence, Electrical and Electronic Engineering, Control and Systems Engineering, Management Science and Operations Research and Computational Theory and Mathematics, having authored 49 papers that have together received 607 indexed citations. Recurring topics across this work include Reinforcement Learning in Robotics (17 papers), Smart Grid Energy Management (12 papers), Advanced Bandit Algorithms Research (9 papers), Optimal Power Flow Distribution (7 papers), Advanced Control Systems Optimization (5 papers), Artificial Intelligence in Games (5 papers), Hybrid Renewable Energy Systems (5 papers) and Integrated Energy Systems Optimization (4 papers). The work is most often cited by research in Energy Engineering and Power Technology (52 citations), Control and Systems Engineering (200 citations), Electrical and Electronic Engineering (395 citations), Virology (24 citations) and Artificial Intelligence (98 citations). Raphaël Fonteneau has collaborated with scholars based in Belgium, United Kingdom and United States. Frequent co-authors include Damien Ernst, Mevludin Glavić, Vincent François-Lavet, Olivier Frédéric, Louis Wehenkel, Susan A. Murphy, Damien Ernst, Antonio Sutera, Axel Gautier and Pierre Geurts. Their work appears in journals such as PLoS ONE, Energy Policy, Annals of Operations Research, Journal of Machine Learning Research and International Journal of Electrical Power & Energy Systems.

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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