André Barreto

994 citations
19 papers · 274 · h-index 8

Impact in

Papers in

André Barreto

19 papers receiving 264 citations

Peers

André Barreto
Comparison fields: 5 of 55
  • Artificial Intelligence 210
  • Computational Theory and Mathematics 49
  • Control and Systems Engineering 50
  • Management Science and Operations Research 22
  • Computational Mathematics 1
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Harm van Seijen Canada
Steven Kapturowski United States
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Riad Akrour Germany
Juan Carlos Santamaria United States
Matthew Riemer United States
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Volkan Ustun United States
Bei Peng United States
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Citations per field
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Citations per year

Countries citing papers authored by André Barreto

Since Specialization
Citations

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

Fields of papers citing papers by André Barreto

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 25 scholars most cited alongside André Barreto, 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 André Barreto Line = papers co-authored together André Barreto links everyone, so they are left out of the graph.

All Works

19 of 19 papers shown
#Work
1 201957
2 202045
3 200741
4
The predictron: end-to-end learning and planning
201727
5 201922
6 201917
7 202112
8
On Efficiency in Hierarchical Reinforcement Learning
202010
9
Value-Aware Loss Function for Model-based Reinforcement Learning
20177
10 20217
11 20187
12 20165
13 20185
14 20125
15
Adaptive Temporal-Difference Learning for Policy Evaluation with Per-State Uncertainty Estimates
20193
16
Coverage as a Principle for Discovering Transferable Behavior in Reinforcement Learning
20211
17 20231
18 20181
19
Knowledge Representation for Reinforcement Learning using General Value Functions
20181

About André Barreto

André Barreto is a scholar working on Artificial Intelligence, Computational Theory and Mathematics, Management Information Systems, Information Systems and Control and Systems Engineering, having authored 19 papers that have together received 274 indexed citations. Recurring topics across this work include Reinforcement Learning in Robotics (16 papers), Evolutionary Algorithms and Applications (7 papers), Artificial Intelligence in Games (2 papers), Supply Chain and Inventory Management (2 papers), Adaptive Dynamic Programming Control (2 papers), Software Engineering Research (2 papers), Explainable Artificial Intelligence (XAI) (2 papers) and Machine Learning and Algorithms (1 paper). The work is most often cited by research in Artificial Intelligence (210 citations), Computational Theory and Mathematics (49 citations), Control and Systems Engineering (50 citations), Management Science and Operations Research (22 citations) and Computational Mathematics (1 citation). André Barreto has collaborated with scholars based in United States, United Kingdom and Canada. Frequent co-authors include David Silver, Charles W. Anderson, Diana Borsa, Doina Precup, Tom Schaul, Shaobo Hou, Rémi Munos, Jonathan J. Hunt, Will Dabney and Hado P. van Hasselt. Their work appears in journals such as Proceedings of the National Academy of Sciences, Artificial Intelligence, BMC Genomics, PolyPublie (École Polytechnique de Montréal) and UCL Discovery (University College London).

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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