Thomas M. Moerland
- Artificial Intelligence top 10%
- Control and Systems Engineering top 10%
- Electrical and Electronic Engineering
- Computer Networks and Communications
- Computer Vision and Pattern Recognition
- Co-authors
- Joost BroekensCatholijn M. JonkerAske PlaatJan N. van RijnJan W. SchoonesKim E. KortekaasVincent T. JanmaatFriedo W. Dekker
- Topics
- Reinforcement Learning in Robotics (5 papers)Evolutionary Algorithms and Applications (3 papers)Multimodal Machine Learning Applications (2 papers)
- Partner nations
- NetherlandsItaly
In The Last Decade
Thomas M. Moerland
8 papers receiving 270 citations
Hit Papers
Peers
Comparison fields: 5 of 75
- Artificial Intelligence 123
- Control and Systems Engineering 64
- Electrical and Electronic Engineering 52
- Computer Networks and Communications 28
- Computer Vision and Pattern Recognition 24
Countries citing papers authored by Thomas M. Moerland
This map shows the geographic impact of Thomas M. Moerland'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 Thomas M. Moerland with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Thomas M. Moerland more than expected).
Fields of papers citing papers by Thomas M. Moerland
This network shows the impact of papers produced by Thomas M. Moerland. 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 Thomas M. Moerland. The network helps show where Thomas M. Moerland may publish in the future.
Co-authorship network of co-authors of Thomas M. Moerland
This figure shows the co-authorship network connecting the top 25 collaborators of Thomas M. Moerland. A scholar is included among the top collaborators of Thomas M. Moerland 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 Thomas M. Moerland. Thomas M. Moerland is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 9 | |
| 2 | Model-based Reinforcement Learning: A Surveybreakdown → | 220 |
| 3 | 21 | |
| 4 | 9 | |
| 5 | 1 | |
| 6 | Learning Multimodal Transition Dynamics for Model-Based Reinforcement Learning | 2 |
| 7 | Fear and hope emerge from anticipation in model-based reinforcement learning | 9 |
| 8 | 1 | |
| 9 | 7 |
About Thomas M. Moerland
Thomas M. Moerland is a scholar working on General Decision Sciences, Management Science and Operations Research and Artificial Intelligence, having authored 9 papers that have together received 279 indexed citations. Recurring topics across this work include Reinforcement Learning in Robotics (5 papers), Evolutionary Algorithms and Applications (3 papers) and Multimodal Machine Learning Applications (2 papers). The work is most often cited by research in Artificial Intelligence (123 citations), Control and Systems Engineering (64 citations) and Computer Science Applications (7 citations). Thomas M. Moerland has collaborated with scholars based in Netherlands and Italy. Frequent co-authors include Joost Broekens, Catholijn M. Jonker, Aske Plaat, Jan N. van Rijn, Jan W. Schoones, Kim E. Kortekaas, Vincent T. Janmaat, Friedo W. Dekker, Astrid van Hylckama Vlieg and Pieter Jonker. Their work appears in journals such as Machine Learning, Frontiers in Artificial Intelligence and Medical Science Educator.
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.