Clune, Jeff

Scaling Instructable Agents Across Many Simulated Worlds

SIMA Team, Raad, M. A., Ahuja, A., Barros, C., Besse, F., Bolt, A., Bolton, A., Brownfield, B., Buttimore, G., Cant, M., et al. (2024) · arXiv

Reinforcement Learning in RoboticsAI-based Problem Solving and Planning

Cited by 9

Multi-task curriculum learning in a complex, visual, hard-exploration domain: Minecraft

Kanitscheider, I., Huizinga, J., Farhi, D., Guss, W. H., Houghton, B., Sampedro, R., Zhokhov, P., Baker, B., Ecoffet, A., Tang, J., et al. (2021) · arXiv

Reinforcement Learning in RoboticsArtificial Intelligence in Games

Cited by 9

Open Questions in Creating Safe Open-ended AI: Tensions Between Control and Creativity

Ecoffet, A., Clune, J., & Lehman, J. (2020) · The 2020 Conference on Artificial Life

Ethics and Social Impacts of AIEvolutionary Algorithms and Applications

Cited by 6

OMNI-EPIC: Open-endedness via Models of human Notions of Interestingness with Environments Programmed in Code

Faldor, M., Zhang, J., Cully, A., & Clune, J. (2024) · arXiv

Reinforcement Learning in RoboticsArtificial Intelligence in Games

Cited by 4

Questioning Representational Optimism in Deep Learning: The Fractured Entangled Representation Hypothesis

Kumar, A., Clune, J., Lehman, J., & Stanley, K. O. (2025) · arXiv

Neural Networks and ApplicationsEvolutionary Algorithms and Applications

Cited by 2

Hyperagents

Zhang, J., Zhao, B., Yang, W., Foerster, J., Clune, J., Jiang, M., Devlin, S., & Shavrina, T. (2026) · arXiv

Evolutionary Algorithms and ApplicationsAI-based Problem Solving and Planning

Cited by 0