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Joel Z. Leibo

Joel Z. Leibo has 2 papers in our library, in 2016; often with Charles Beattie, David Silver, Denis Teplyashin.

2 papers in our library · 2016

OpenAlex · ORCID · Wikidata · h-index 35 · 195 works · 7,571 citations OpenAlex, 2026-10-14

📄 2 works in our library · 🔗 Cited 218 times in our library · 5 more in our library without a page yet · ⭐ Paper Stars ranking: #4499

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

🏢Google DeepMindLondon · United Kingdom2013–2026 · 71 papersEmployment since 2013 · Research Scientistcurrent🏢GoogleLondon · United Kingdom2023–2026 · 5 paperscurrent🎓University of TorontoToronto · Canada2025–2026 · 2 paperscurrent🎓Massachusetts Institute of TechnologyCambridge · United States2008–2019 · 32 papersEducation 2008–2013 · PhD, Brain & Cognitive Sciences🏢GoogleMountain View · United States2016–2020 · 12 papers🔬McGovern Institute for Brain ResearchCambridge · United States2012–2016 · 10 papers🎓King's College LondonLondon · United Kingdom2024–2025 · 5 papers

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 4 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 2The field of science this work belongs to (OpenAlex fields).Artificial Intelligence · 2 worksComputer Science · 2 works

 Works by year

2010s

20162 papers

 Papers

The most cited first (OpenAlex and our library).

  1. 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…
  2. DeepMind Lab (2016 · 134 citations)
    Studying embodied intelligence with physical robots is slow and expensive, and building detailed virtual worlds for AI research has required heavy engineering.

All works

7 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. Human-level performance in 3D multiplayer games with population-based reinforcement learning ↗Science · no summary yetMax Jaderberg et al.2019 · 682 citations
  2. Reinforcement Learning with Unsupervised Auxiliary TasksarXiv:1611.05397Max Jaderberg, Volodymyr Mnih, Wojciech Marian Czarnecki, et al.2016 · 319 citations
  3. Using Fast Weights to Attend to the Recent Past ↗arXiv (Cornell University) · no summary yetJimmy Lei Ba et al.2016 · 168 citations
  4. Unsupervised Predictive Memory in a Goal-Directed Agent ↗arXiv (Cornell University) · no summary yetGreg Wayne et al.2018 · 140 citations
  5. DeepMind LabarXiv:1612.03801Charles Beattie, Joel Z. Leibo, Denis Teplyashin, et al.2016 · 134 citations
  6. Kickstarting Deep Reinforcement Learning ↗arXiv (Cornell University) · no summary yetSimon Schmitt et al.2018 · 51 citations
  7. A Case Study on Emergent Cheating and Whistleblowing in Autonomous Research Swarms ↗arXiv:2609.04170 · no summary yetDavide Paglieri et al.2026

 Co-authors here

11 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 218 times (of the 149,439 papers whose bibliographies we read); 31 citing papers with a page here →.

Updated

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