André Barreto

950 citations
16 papers · 223 · h-index 8

Impact in

Papers in

    • Reinforcement Learning in Robotics 14
    • Evolutionary Algorithms and Applications 6
    • Explainable Artificial Intelligence (XAI) 2
    • Data Stream Mining Techniques 1
    • Artificial Intelligence in Games 1
    • Supply Chain and Inventory Management 2

André Barreto

16 papers receiving 216 citations

Peers

André Barreto
Comparison fields: 5 of 48
  • Artificial Intelligence 176
  • Computational Theory and Mathematics 44
  • Control and Systems Engineering 46
  • Computational Mathematics 1
  • Management Science and Operations Research 19
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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

16 of 16 papers shown
#Work
1 201950
2 202037
3 200735
4
The predictron: end-to-end learning and planning
201725
5 201919
6 201915
7
On Efficiency in Hierarchical Reinforcement Learning
20209
8
Value-Aware Loss Function for Model-based Reinforcement Learning
20177
9 20187
10 20165
11 20124
12 20184
13
Adaptive Temporal-Difference Learning for Policy Evaluation with Per-State Uncertainty Estimates
20193
14
Coverage as a Principle for Discovering Transferable Behavior in Reinforcement Learning
20211
15
Knowledge Representation for Reinforcement Learning using General Value Functions
20181
16 20181

About André Barreto

André Barreto is a scholar working on Artificial Intelligence, Management Information Systems, Information Systems, Computational Theory and Mathematics and Molecular Biology, having authored 16 papers that have together received 223 indexed citations. Recurring topics across this work include Reinforcement Learning in Robotics (14 papers), Evolutionary Algorithms and Applications (6 papers), Supply Chain and Inventory Management (2 papers), Software Engineering Research (2 papers), Explainable Artificial Intelligence (XAI) (2 papers), Data Stream Mining Techniques (1 paper), Artificial Intelligence in Games (1 paper) and Adaptive Dynamic Programming Control (1 paper). The work is most often cited by research in Artificial Intelligence (176 citations), Computational Theory and Mathematics (44 citations), Control and Systems Engineering (46 citations), Computational Mathematics (1 citation) and Management Science and Operations Research (19 citations). André Barreto has collaborated with scholars based in United States, Canada and United Kingdom. Frequent co-authors include David Silver, Charles W. Anderson, Tom Schaul, Doina Precup, Diana Borsa, Jonathan J. Hunt, Rémi Munos, Shaobo Hou, Will Dabney and Hado P. van Hasselt. Their work appears in journals such as Artificial Intelligence, BMC Genomics, Proceedings of the National Academy of Sciences, UCL Discovery (University College London) and arXiv (Cornell University).

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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