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 practical guide to multi-objective reinforcement learning and planning
2022158 citationsConor F. Hayes, Roxana Rădulescu et al.Virtual Community of Pathological Anatomy (University of Castilla La Mancha)profile →
Peers — A (Enhanced Table)
Peers by citation overlap · career bar shows stage (early→late)
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Countries citing papers authored by Marcello Restelli
Since
Specialization
Citations
This map shows the geographic impact of Marcello Restelli'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 Marcello Restelli with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Marcello Restelli more than expected).
Fields of papers citing papers by Marcello Restelli
This network shows the impact of papers produced by Marcello Restelli. 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 Marcello Restelli. The network helps show where Marcello Restelli may publish in the future.
Co-authorship network of co-authors of Marcello Restelli
This figure shows the co-authorship network connecting the top 25 collaborators of Marcello Restelli.
A scholar is included among the top collaborators of Marcello Restelli 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 Marcello Restelli. Marcello Restelli is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Hayes, Conor F., Roxana Rădulescu, Eugenio Bargiacchi, et al.. (2022). A practical guide to multi-objective reinforcement learning and planning. Virtual Community of Pathological Anatomy (University of Castilla La Mancha).158 indexed citations breakdown →
Restelli, Marcello, et al.. (2021). Meta-Reinforcement Learning by Tracking Task Non-stationarity. Virtual Community of Pathological Anatomy (University of Castilla La Mancha).2 indexed citations
5.
Metelli, Alberto Maria, et al.. (2021). Subgaussian and Differentiable Importance Sampling for Off-Policy Evaluation and Learning. Neural Information Processing Systems. 34.4 indexed citations
6.
Pirotta, Matteo, et al.. (2021). Gaussian Approximation for Bias Reduction in Q-Learning. Journal of Machine Learning Research. 22(277). 1–51.4 indexed citations
Restelli, Marcello, et al.. (2020). Balancing Learning Speed and Stability in Policy Gradient via Adaptive Exploration. Archivio Istituzionale della Ricerca (Universita Degli Studi Di Milano). 108. 1188–1199.5 indexed citations
9.
Metelli, Alberto Maria, et al.. (2020). Truly Batch Model-Free Inverse Reinforcement Learning about Multiple Intentions. Archivio istituzionale della ricerca (Alma Mater Studiorum Università di Bologna). 108. 2359–2369.5 indexed citations
10.
Metelli, Alberto Maria, et al.. (2020). Importance Sampling Techniques for Policy Optimization. Journal of Machine Learning Research. 21(141). 1–75.15 indexed citations
Restelli, Marcello, et al.. (2019). Transfer of Samples in Policy Search via Multiple Importance Sampling. International Conference on Machine Learning. 97. 6264–6274.4 indexed citations
13.
Pirotta, Matteo, et al.. (2018). Importance Weighted Transfer of Samples in Reinforcement Learning. Virtual Community of Pathological Anatomy (University of Castilla La Mancha).2 indexed citations
14.
Restelli, Marcello, et al.. (2016). Estimating the maximum expected value through Gaussian approximation. Virtual Community of Pathological Anatomy (University of Castilla La Mancha). 1032–1040.22 indexed citations
15.
Pirotta, Matteo, Marcello Restelli, & Luca Bascetta. (2013). Adaptive Step-Size for Policy Gradient Methods. Virtual Community of Pathological Anatomy (University of Castilla La Mancha). 26. 1394–1402.22 indexed citations
16.
Pirotta, Matteo, et al.. (2013). Safe Policy Iteration. Virtual Community of Pathological Anatomy (University of Castilla La Mancha). 307–315.12 indexed citations
17.
Gatti, Nicola & Marcello Restelli. (2011). Equilibrium Approximation in Extensive-Form Simulation-Based Games. Virtual Community of Pathological Anatomy (University of Castilla La Mancha). 199–206.
18.
Castelletti, Andrea, et al.. (2009). An emulation modelling approach to reduce the complexity of a 3D hydrodynamic-ecological model of a reservoir. Virtual Community of Pathological Anatomy (University of Castilla La Mancha). 1–9.6 indexed citations
19.
Lazaric, Alessandro, Enrique Muñoz de Cote, Fabio Dercole, & Marcello Restelli. (2007). Bifurcation Analysis of Reinforcement Learning Agents in the Selten's Horse Game.. Adaptive Agents and Multi-Agents Systems. 129–144.1 indexed citations
20.
Bonarini, Andrea, Matteo Matteucci, & Marcello Restelli. (2004). A novel model to rule behavior interaction. Virtual Community of Pathological Anatomy (University of Castilla La Mancha). 199–206.3 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.