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Volodymyr Mnih

Volodymyr Mnih has 2 papers in our library, in 2015–2016; often with Charles Beattie, David Silver, Joel Z Leibo.

2 papers in our library · 2015–2016

OpenAlex · h-index 30 · 53 works · 44,626 citations OpenAlex, 2026-10-14

📄 2 works in our library · 🔗 Cited 1,031 times in our library · 11 more in our library without a page yet · ⭐ Paper Stars ranking: #3293

The profile is the library's match of the name; two people with one name are not always told apart.

Elsewhere: Google Scholar · Semantic Scholar · dblp · ACL Anthology

 Affiliations

🏢GoogleMountain View · United States2014–2021 · 14 papers🎓University of TorontoToronto · Canada2006–2013 · 9 papers🏢Google DeepMindLondon · United Kingdom2013–2021 · 8 papers

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 2 more institutions on single bylines are not listed.

 Fields, topics and concepts

Of its works in our library, with the number of works on each.

📚 Shelves 3The field of science this work belongs to (OpenAlex fields).Computer Science · 2 worksArtificial Intelligence · 2 worksHardware and Architecture · 1 work

 Works by year

2010s

20161 paper20151 paper

 Papers

The most cited first (OpenAlex and our library).

  1. Massively Parallel Methods for Deep Reinforcement Learning (2015 · 427 citations)
    DQN, the deep Q-learning agent that learns Atari 2600 games from raw pixels, had only been run on a single machine.
  2. Reinforcement Learning with Unsupervised Auxiliary Tasks (2016 · 319 citations)
    Deep RL agents usually learn only from extrinsic reward, which is often sparse. That leaves most of the information in an agent's observation stream unused. Jaderberg et al. (DeepMind) propose UNREAL (UNsupervised REinforcement and Auxiliary Learning), which extends the A3C actor-critic with extra objectives that…

All works

13 works of theirs in our library, the most cited first; 2 with a page here, the others link their original until we summarise them.

  1. Playing Atari with Deep Reinforcement Learning ↗arXiv:1312.5602 · no summary yetVolodymyr Mnih et al.2013 · 5,030 citations
  2. Recurrent Models of Visual Attention ↗arXiv (Cornell University) · no summary yetVolodymyr Mnih et al.2014 · 2,696 citations
  3. Asynchronous Methods for Deep Reinforcement Learning ↗arXiv:1602.01783 · ICML 2016 · no summary yetVolodymyr Mnih et al.2016 · 1,955 citations
  4. Multiple Object Recognition with Visual Attention ↗arXiv (Cornell University) · no summary yetJimmy Lei Ba, Volodymyr Mnih, Koray Kavukcuoglu2014 · 698 citations
  5. Massively Parallel Methods for Deep Reinforcement LearningarXiv:1507.04296Arun Nair, Praveen Srinivasan, Sam Blackwell, et al.2015 · 427 citations
  6. Reinforcement Learning with Unsupervised Auxiliary TasksarXiv:1611.05397Max Jaderberg, Volodymyr Mnih, Wojciech Marian Czarnecki, et al.2016 · 319 citations
  7. Sample Efficient Actor-Critic with Experience Replay ↗arXiv (Cornell University) · no summary yetZiyu Wang et al.2016 · 220 citations
  8. Using Fast Weights to Attend to the Recent Past ↗arXiv (Cornell University) · no summary yetJimmy Lei Ba et al.2016 · 168 citations
  9. Unsupervised Learning of Object Keypoints for Perception and Control ↗arXiv (Cornell University) · no summary yetTejas D. Kulkarni et al.2019 · 109 citations
  10. Policy Distillation ↗arXiv (Cornell University) · no summary yetAndrei A. Rusu et al.2015 · 108 citations
  11. Combining policy gradient and Q-learning ↗arXiv (Cornell University) · no summary yetBrendan O’Donoghue et al.2016 · 98 citations
  12. Strategic Attentive Writer for Learning Macro-Actions ↗arXiv (Cornell University) · no summary yetAlexander - et al.2016 · 89 citations
  13. Topological map learning from outdoor image sequences ↗Journal of Field Robotics · no summary yetXuming He, Richard S. Zemel, Volodymyr Mnih2006 · 23 citations

 Co-authors here

8 co-authors with a page, the most papers together first.

Venues

Where their works here were published, the most works first, with the editions of those that have a page.

 Cited by

Papers of our library cite their works 1,031 times (of the 149,439 papers whose bibliographies we read); 110 citing papers with a page here →.

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