Mathieu Reymond

437 citations
7 papers · 213 · 1 hit paper · h-index 5

Impact in

Papers in

Mathieu Reymond

7 papers receiving 208 citations

Mathieu Reymond's Hit Papers

A practical guide to multi-objective reinforcement learning and planning 2022 · 172 citations
1720+1+2Years since publication50100150

Peers

Mathieu Reymond
Comparison fields: 5 of 67
  • Computational Theory and Mathematics 45
  • Artificial Intelligence 74
  • Industrial and Manufacturing Engineering 21
  • Management Science and Operations Research 20
  • Health Informatics 2
Replace Conor F. Hayes with:
Conor F. Hayes Belgium
Johan Källström Sweden
Eugenio Bargiacchi Netherlands
Miklós Krész Hungary
Erwin Schoitsch Austria
Huáscar Espinoza Spain
Kazuteru Miyazaki Japan
Addi Ait‐Mlouk Morocco
Hangyu Mao China
Ruben Glatt United States
Mathieu Reymond relative to Conor F. Hayes Belgium Conor F. Hayes's profile →
Citations per field
00.5×
Conor F. Hayes · 1×
Citations per year

Countries citing papers authored by Mathieu Reymond

Since Specialization
Citations

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

Fields of papers citing papers by Mathieu Reymond

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 22 scholars most cited alongside Mathieu Reymond, 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 Mathieu Reymond Line = papers co-authored together Mathieu Reymond links everyone, so they are left out of the graph.

All Works

7 of 7 papers shown
#Work
1
A practical guide to multi-objective reinforcement learning and planning
Hit paper breakdown →
2022172
2
Pareto-DQN: Approximating the Pareto front in complex multi-objective decision problems
201911
3 202310
4 20217
5
Reinforcement Learning for Demand Response of Domestic Household Appliances
20187
6 20244
7 20232

About Mathieu Reymond

Mathieu Reymond is a scholar working on Computational Theory and Mathematics, Artificial Intelligence, Automotive Engineering, Control and Systems Engineering and Ocean Engineering, having authored 7 papers that have together received 213 indexed citations. Recurring topics across this work include Advanced Multi-Objective Optimization Algorithms (5 papers), Reinforcement Learning in Robotics (5 papers), Adaptive Dynamic Programming Control (2 papers), Artificial Intelligence in Games (1 paper), Evolutionary Algorithms and Applications (1 paper), Reservoir Engineering and Simulation Methods (1 paper), Transportation and Mobility Innovations (1 paper) and Energy Efficiency and Management (1 paper). The work is most often cited by research in Computational Theory and Mathematics (45 citations), Artificial Intelligence (74 citations), Industrial and Manufacturing Engineering (21 citations), Management Science and Operations Research (20 citations) and Health Informatics (2 citations). Mathieu Reymond has collaborated with scholars based in Belgium, Ireland and Netherlands. Frequent co-authors include Ann Nowé, Diederik M. Roijers, Conor F. Hayes, Enda Howley, Patrick Mannion, Roxana Rădulescu, Richard Dazeley, Athirai A. Irissappane, Gabriel de Oliveira Ramos and Marcello Restelli. Their work appears in journals such as Autonomous Agents and Multi-Agent Systems, Expert Systems with Applications and VUBIR (Vrije Universiteit Brussel).

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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