Marc G. Bellemare Google Scholar: h-index, Total Citations, and Citation Map
Marc G. Bellemare's h-index is 50 (81 i10-index, 68,990+ total citations across 121+ publications) according to Google Scholar as of October 2026. Marc G. Bellemare is affiliated with Reliant AI.
Marc G. Bellemare is a researcher affiliated with Reliant AI, specializing in various fields. Their work has been cited 68,990 times. This profile visualizes their global influence, highlighting strong citation networks in United States.
Marc G. Bellemare's Citation Metrics
Bibliometric impact based on 121 indexed publications.
- H-Index
- 50
- i10-Index
- 81
- Total Citations
- 68,990
- Citing Countries
- 72
As of October 2026.
Marc G. Bellemare has an h-index of 50 and 68,990 total citations across 121 publications, with research cited by institutions in 72 countries.
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Global Impact Map
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Human-level control through deep reinforcement learning
201542,954
Top Citing Countries
Top Citing Institutions
Visa Evidence Package
Views and exports tuned for EB-1A, O-1A, and EB-2 NIW petitions. Sustained acclaim, geographic reach, and independent-citation filtering are the strongest evidence categories immigration adjudicators look for.
Significant Contributions
Auto-detected research lines — a seminal paper and the follow-up work building on it. Review and edit before using in a petition. Each Free PDF opens in a new tab — EB-1A organises this into the structure USCIS applies to Criterion 5 of 8 CFR § 204.5(h)(3)(v); EB-1B re-frames it under § 204.5(i)(3) (outstanding researcher); NIW presents it under prong 2 of Matter of Dhanasar.
16 citing papers could not be classified (no author data) — excluded from the percentages above.
The researcher advanced deep reinforcement learning by introducing novel operators to increase the action gap, establishing a foundational framework for robust policy optimization and open-source research tools.
The researcher pioneered distributional reinforcement learning via quantile regression, establishing a foundational framework subsequently applied to autonomous navigation and continuous latent space representation learning.
The researcher advanced deep reinforcement learning by introducing count-based exploration with neural density models and establishing standardized evaluation protocols for general agents.
Citation trend (last 10 years)Click to expand
Citation Trend (Last 10 Years)
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About Marc G. Bellemare's research
Marc G. Bellemare is a researcher at Reliant AI. Their work has been cited 68,990 times across 121 publications (h-index 50), according to Google Scholar.
Their most-cited work, “Human-level control through deep reinforcement learning” (2015), has accumulated 42,954 citations. Other influential works include “The Arcade Learning Environment: An Evaluation Platform for General Agents” (2013) with 4,550 citations and “A distributional perspective on reinforcement learning” (2017) with 2,693 citations.
Citations of Marc G. Bellemare's research come primarily from United States, China and United Kingdom, reflecting international research impact across 5+ countries. The interactive citation map above shows the full geographic distribution of the institutions citing this work.
