Hamid Reza Maei
- Artificial Intelligence top 2%
- Cognitive Neuroscience top 5%
- Electrical and Electronic Engineering
- Cellular and Molecular Neuroscience top 10%
- Computational Theory and Mathematics top 2%
- Co-authors
- Richard S. SuttonCsaba SzepesváriShalabh BhatnagarZheng WenDaniel O’NeillDavid SilverDoina PrecupCátia M. Teixeira
- Topics
- Reinforcement Learning in Robotics (6 papers)Advanced Bandit Algorithms Research (4 papers)Neuroscience and Neuropharmacology Research (3 papers)
In The Last Decade
Hamid Reza Maei
11 papers receiving 1.2k citations
Peers
Comparison fields: 5 of 98
- Artificial Intelligence 528
- Cognitive Neuroscience 302
- Electrical and Electronic Engineering 278
- Cellular and Molecular Neuroscience 243
- Computational Theory and Mathematics 210
Countries citing papers authored by Hamid Reza Maei
This map shows the geographic impact of Hamid Reza Maei'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 Hamid Reza Maei with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Hamid Reza Maei more than expected).
Fields of papers citing papers by Hamid Reza Maei
This network shows the impact of papers produced by Hamid Reza Maei. 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 Hamid Reza Maei. The network helps show where Hamid Reza Maei may publish in the future.
Co-authorship network of co-authors of Hamid Reza Maei
This figure shows the co-authorship network connecting the top 25 collaborators of Hamid Reza Maei. A scholar is included among the top collaborators of Hamid Reza Maei 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 Hamid Reza Maei. Hamid Reza Maei is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 200 | |
| 2 | 19 | |
| 3 | 94 | |
| 4 | 56 | |
| 5 | Convergent Temporal-Difference Learning with Arbitrary Smooth Function Approximation | 81 |
| 6 | 18 | |
| 7 | 169 | |
| 8 | 258 | |
| 9 | A convergent O ( n ) algorithm for off-policy temporal-difference learning with linear function approximation | 63 |
| 10 | A Convergent O(n) Temporal-difference Algorithm for Off-policy Learning with Linear Function Approximation | 71 |
| 11 | 229 |
About Hamid Reza Maei
Hamid Reza Maei is a scholar working on Management Science and Operations Research, Neurology and Artificial Intelligence, having authored 11 papers that have together received 1.3k indexed citations. Recurring topics across this work include Reinforcement Learning in Robotics (6 papers), Advanced Bandit Algorithms Research (4 papers) and Neuroscience and Neuropharmacology Research (3 papers). The work is most often cited by research in Artificial Intelligence (528 citations), Cognitive Neuroscience (302 citations) and Behavioral Neuroscience (52 citations). Hamid Reza Maei has collaborated with scholars based in Canada, India and Hungary. Frequent co-authors include Richard S. Sutton, Csaba Szepesvári, Shalabh Bhatnagar, Zheng Wen, Daniel O’Neill, David Silver, Doina Precup, Cátia M. Teixeira, Paul W. Frankland and Nohjin Kee. Their work appears in journals such as Journal of Neuroscience, IEEE Transactions on Smart Grid and Neural Computation.
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.