Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies
if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the
same subfield and year (this is the minimum needed to enter the top 1%, not the average
within it), or reaches the top citation threshold in at least one of its specific research
topics.
A Comprehensive Survey of Multiagent Reinforcement Learning
20081.3k citationsLucian Buşoniu, Robert Babuška et al.profile →
A Survey of Actor-Critic Reinforcement Learning: Standard and Natural Policy Gradients
2012689 citationsLucian Buşoniu, Gabriel A. D. Lopes et al.profile →
Countries citing papers authored by Robert Babuška
Since
Specialization
Citations
This map shows the geographic impact of Robert Babuška'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 Robert Babuška with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Robert Babuška more than expected).
This network shows the impact of papers produced by Robert Babuška. 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 Robert Babuška. The network helps show where Robert Babuška may publish in the future.
Co-authorship network of co-authors of Robert Babuška
This figure shows the co-authorship network connecting the top 25 collaborators of Robert Babuška.
A scholar is included among the top collaborators of Robert Babuška 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 Robert Babuška. Robert Babuška is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Bruin, Tim de, Jens Kober, Karl Tuyls, & Robert Babuška. (2016). Off-policy experience retention for deep actor-critic learning. Neural Information Processing Systems.3 indexed citations
10.
Bruin, Tim de, Jens Kober, Karl Tuyls, & Robert Babuška. (2015). The importance of experience replay database composition in deep reinforcement learning. Neural Information Processing Systems.44 indexed citations
11.
Lopes, Gabriel A. D., et al.. (2012). Energy-balancing passivity-based control through reinforcement learning. arXiv (Cornell University).1 indexed citations
Nelles, Oliver, Alexander Fink, Robert Babuška, & M. Setnes. (1999). Comparision of Two Construction Algorithms for Takagi-Sugeno Fuzzy Models. International Journal of Applied Mathematics and Computer Science. 10(4). 835–855.29 indexed citations
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.