Eugenio Bargiacchi
About
In The Last Decade
Eugenio Bargiacchi
6 papers receiving 176 citations
Hit Papers
Peers
Comparison fields: 5 of 65
- Artificial Intelligence 71
- Computational Theory and Mathematics 33
- Electrical and Electronic Engineering 30
- Computer Networks and Communications 29
- Industrial and Manufacturing Engineering 21
Countries citing papers authored by Eugenio Bargiacchi
This map shows the geographic impact of Eugenio Bargiacchi'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 Eugenio Bargiacchi with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Eugenio Bargiacchi more than expected).
Fields of papers citing papers by Eugenio Bargiacchi
This network shows the impact of papers produced by Eugenio Bargiacchi. 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 Eugenio Bargiacchi. The network helps show where Eugenio Bargiacchi may publish in the future.
Co-authorship network of co-authors of Eugenio Bargiacchi
This figure shows the co-authorship network connecting the top 25 collaborators of Eugenio Bargiacchi. A scholar is included among the top collaborators of Eugenio Bargiacchi 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 Eugenio Bargiacchi. Eugenio Bargiacchi is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | A practical guide to multi-objective reinforcement learning and planning breakdown → | 158 |
| 2 | 3 | |
| 3 | 11 | |
| 4 | AI-toolbox: A C++ library for reinforcement learning and planning (with Python Bindings) | 6 |
| 5 | Reinforcement Learning 101 with a Virtual Reality Game | 3 |
| 6 | A Virtual Maze Game to Explain Reinforcement Learning | 0 |
| 7 | Learning to Coordinate with Coordination Graphs in Repeated Single-Stage Multi-Agent Decision Problems | 9 |
| 8 | 0 |
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.