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.
Deep Reinforcement Learning That Matters
2018814 citationsPeter Henderson, Riashat Islam et al.Proceedings of the AAAI Conference on Artificial Intelligenceprofile →
An Introduction to Deep Reinforcement Learning
2018811 citationsVincent François-Lavet, Peter Henderson et al.LA Referencia (Red Federada de Repositorios Institucionales de Publicaciones Científicas)profile →
An Introduction to Deep Reinforcement Learning
2018343 citationsVincent François-Lavet, Peter Henderson et al.now publishers, Inc. eBooksprofile →
Peers — A (Enhanced Table)
Peers by citation overlap · career bar shows stage (early→late)
cites ·
hero ref
This map shows the geographic impact of Riashat Islam'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 Riashat Islam with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Riashat Islam more than expected).
This network shows the impact of papers produced by Riashat Islam. 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 Riashat Islam. The network helps show where Riashat Islam may publish in the future.
Co-authorship network of co-authors of Riashat Islam
This figure shows the co-authorship network connecting the top 25 collaborators of Riashat Islam.
A scholar is included among the top collaborators of Riashat Islam 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 Riashat Islam. Riashat Islam is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Goyal, Anirudh, Riashat Islam, Zafarali Ahmed, et al.. (2019). InfoBot: Transfer and Exploration via the Information Bottleneck. arXiv (Cornell University).8 indexed citations
3.
Goyal, Anirudh, Riashat Islam, Zafarali Ahmed, et al.. (2019). InfoBot: Structured Exploration in ReinforcementLearning Using Information Bottleneck.1 indexed citations
4.
François-Lavet, Vincent, Peter Henderson, Riashat Islam, Marc G. Bellemare, & Joëlle Pineau. (2018). An Introduction to Deep Reinforcement Learning. LA Referencia (Red Federada de Repositorios Institucionales de Publicaciones Científicas). 11(3-4). 219–354.811 indexed citations breakdown →
5.
Henderson, Peter, Riashat Islam, Philip Bachman, et al.. (2018). Deep Reinforcement Learning That Matters. Proceedings of the AAAI Conference on Artificial Intelligence. 32(1).814 indexed citations breakdown →
Ahmed, Zafarali, et al.. (2018). RE-EVALUATE: Reproducibility in Evaluating Reinforcement Learning Algorithms.8 indexed citations
8.
François-Lavet, Vincent, Peter Henderson, Riashat Islam, Marc G. Bellemare, & Joëlle Pineau. (2018). An Introduction to Deep Reinforcement Learning. now publishers, Inc. eBooks.343 indexed citations breakdown →
9.
Henderson, Peter, Riashat Islam, Philip Bachman, et al.. (2017). Deep Reinforcement Learning that Matters. arXiv (Cornell University). 32(1). 3207–3214.111 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.