Marlos C. Machado
- Artificial Intelligence top 10%
- Aerospace Engineering
- Computer Vision and Pattern Recognition
- Ocean Engineering
- Control and Systems Engineering
- Co-authors
- Marc G. BellemarePablo Samuel CastroSubhodeep MoitraJun GongZiyu WangSameera PondaSalvatore CandidoMichael Bowling
- Topics
- Reinforcement Learning in Robotics (11 papers)Artificial Intelligence in Games (9 papers)Advanced Bandit Algorithms Research (6 papers)
- Partner nations
- CanadaUnited StatesBrazil
In The Last Decade
Marlos C. Machado
20 papers receiving 277 citations
Peers
Comparison fields: 5 of 63
- Artificial Intelligence 154
- Aerospace Engineering 66
- Computer Vision and Pattern Recognition 41
- Ocean Engineering 38
- Control and Systems Engineering 36
Countries citing papers authored by Marlos C. Machado
This map shows the geographic impact of Marlos C. Machado'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 Marlos C. Machado with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Marlos C. Machado more than expected).
Fields of papers citing papers by Marlos C. Machado
This network shows the impact of papers produced by Marlos C. Machado. 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 Marlos C. Machado. The network helps show where Marlos C. Machado may publish in the future.
Co-authorship network of co-authors of Marlos C. Machado
This figure shows the co-authorship network connecting the top 25 collaborators of Marlos C. Machado. A scholar is included among the top collaborators of Marlos C. Machado 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 Marlos C. Machado. Marlos C. Machado is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 6 | |
| 2 | 2 | |
| 3 | 2 | |
| 4 | Beyond Variance Reduction: Understanding the True Impact of Baselines on Policy Optimization | 1 |
| 5 | 3 | |
| 6 | Exploration in Reinforcement Learning with Deep Covering Options | 11 |
| 7 | 164 | |
| 8 | 1 | |
| 9 | 1 | |
| 10 | Eigenoption Discovery through the Deep Successor Representation | 2 |
| 11 | 27 | |
| 12 | 23 | |
| 13 | 7 | |
| 14 | 1 | |
| 15 | Characterizing and Modeling Ag ents in Digital Games | 1 |
| 16 | 24 | |
| 17 | 3 | |
| 18 | 6 | |
| 19 | Player Modeling: What is it? How to do it? | 1 |
| 20 | Occurrence of Listeria monocytogenes and other Listeria spp. in brains of cattle with suspected BSE. | 0 |
About Marlos C. Machado
Marlos C. Machado is a scholar working on Artificial Intelligence, Management Science and Operations Research and Biotechnology, having authored 21 papers that have together received 291 indexed citations. Recurring topics across this work include Reinforcement Learning in Robotics (11 papers), Artificial Intelligence in Games (9 papers) and Advanced Bandit Algorithms Research (6 papers). The work is most often cited by research in Artificial Intelligence (154 citations), Aerospace Engineering (66 citations) and Ocean Engineering (38 citations). Marlos C. Machado has collaborated with scholars based in Canada, United States and Brazil. Frequent co-authors include Marc G. Bellemare, Pablo Samuel Castro, Subhodeep Moitra, Jun Gong, Ziyu Wang, Sameera Ponda, Salvatore Candido, Michael Bowling, Luiz Chaimowicz and Erik Talvitie. Their work appears in journals such as Nature, Artificial Intelligence and Machine Learning.
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.