Foerster, Jakob

Counterfactual Multi-Agent Policy Gradients

Foerster, J., Farquhar, G., Afouras, T., Nardelli, N., & Whiteson, S. (2018) · Proceedings of the AAAI Conference on Artificial Intelligence

Reinforcement Learning in RoboticsArtificial Intelligence in Games

Cited by 1,873

Learning to Communicate with Deep Multi-Agent Reinforcement Learning

Foerster, J. N., Assael, Y. M., de Freitas, N., & Whiteson, S. (2016) · arXiv

Reinforcement Learning in RoboticsMulti-Agent Systems and Negotiation

Cited by 1,217

The StarCraft Multi-Agent Challenge

Samvelyan, M., Rashid, T., Schroeder de Witt, C., Farquhar, G., Nardelli, N., Rudner, T. G. J., Hung, C.-M., Torr, P. H. S., Foerster, J., & Whiteson, S. (2019) · International Joint Conference on Autonomous Agents and Multiagent Systems

Reinforcement Learning in RoboticsArtificial Intelligence in Games

Cited by 453

Multi-Agent Risks from Advanced AI

Hammond, L., Chan, A., Clifton, J., Hoelscher-Obermaier, J., Khan, A., McLean, E., Smith, C., Barfuss, W., Foerster, J., Gavenčiak, T., et al. (2025) · arXiv

Ethics and Social Impacts of AIMulti-Agent Systems and Negotiation

Cited by 305

Learning with Opponent-Learning Awareness

Foerster, J., Chen, R. Y., Al-Shedivat, M., Whiteson, S., Abbeel, P., & Mordatch, I. (2018) · International Joint Conference on Autonomous Agents and Multiagent Systems

Reinforcement Learning in RoboticsGame Theory and Applications

Cited by 215

The AI Scientist: Towards Fully Automated Open-Ended Scientific Discovery

Lu, C., Lu, C., Lange, R. T., Foerster, J., Clune, J., & Ha, D. (2024) · arXiv

Artificial Intelligence ApplicationsTopic Modeling

Cited by 118

Towards end-to-end automation of AI research

Lu, C., Lu, C., Lange, R. T., Yamada, Y., Hu, S., Foerster, J., Ha, D., & Clune, J. (2026) · Nature

Artificial Intelligence ApplicationsScientific Computing and Data Management

Cited by 116

Measuring what Matters: Construct Validity in Large Language Model Benchmarks

Bean, A. M., Kearns, R. O., Romanou, A., Hafner, F. S., Mayne, H., Batzner, J., Foroutan Eghlidi, N., Schmitz, C., Korgul, K., Batra, H., et al. (2025) · Advances in Neural Information Processing Systems 38

Topic ModelingNatural Language Processing Techniques

Cited by 110

The AI Scientist-v2: Workshop-Level Automated Scientific Discovery via Agentic Tree Search

Yamada, Y., Lange, R. T., Lu, C., Hu, S., Lu, C., Foerster, J., Clune, J., & Ha, D. (2025) · arXiv

Artificial Intelligence ApplicationsAcademic Writing and Publishing

Cited by 32

Trajectory Diversity for Zero-Shot Coordination

Lupu, A., Hu, H., & Foerster, J. (2021) · International Joint Conference on Autonomous Agents and Multiagent Systems

Reinforcement Learning in RoboticsGame Theory and Applications

Cited by 28

Evolving Curricula with Regret-Based Environment Design

Parker-Holder, J., Jiang, M., Dennis, M., Samvelyan, M., Foerster, J., Grefenstette, E., & Rocktäschel, T. (2022) · arXiv

Reinforcement Learning in RoboticsEvolutionary Algorithms and Applications

Cited by 28

Replay-Guided Adversarial Environment Design

Jiang, M., Dennis, M., Parker-Holder, J., Foerster, J., Grefenstette, E., & Rocktäschel, T. (2021) · arXiv

Reinforcement Learning in RoboticsArtificial Intelligence in Games

Cited by 21

MLGym: A New Framework and Benchmark for Advancing AI Research Agents

Nathani, D., Madaan, L., Roberts, N., Bashlykov, N., Menon, A., Moens, V., Budhiraja, A., Magka, D., Vorotilov, V., Chaurasia, G., et al. (2025) · arXiv

Artificial Intelligence ApplicationsReinforcement Learning in Robotics

Cited by 15

MAESTRO: Open-Ended Environment Design for Multi-Agent Reinforcement Learning

Samvelyan, M., Khan, A., Dennis, M., Jiang, M., Parker-Holder, J., Foerster, J., Raileanu, R., & Rocktäschel, T. (2023) · arXiv

Reinforcement Learning in RoboticsArtificial Intelligence in Games

Cited by 8

Artificial Generational Intelligence: Cultural Accumulation in Reinforcement Learning

Cook, J., Lu, C., Hughes, E., Leibo, J. Z., & Foerster, J. (2024) · arXiv

Reinforcement Learning in RoboticsEvolutionary Algorithms and Applications

Cited by 6

Craftax: A Lightning-Fast Benchmark for Open-Ended Reinforcement Learning

Matthews, M., Beukman, M., Ellis, B., Samvelyan, M., Jackson, M., Coward, S., & Foerster, J. (2024) · arXiv

Reinforcement Learning in RoboticsArtificial Intelligence in Games

Cited by 4

Equivariant Networks for Zero-Shot Coordination

Muglich, D., de Witt, C. S., van der Pol, E., Whiteson, S., & Foerster, J. (2022) · arXiv

Reinforcement Learning in RoboticsDomain Adaptation and Few-Shot Learning

Cited by 3

Near to Mid-term Risks and Opportunities of Open-Source Generative AI

Eiras, F., Petrov, A., Vidgen, B., de Witt, C. S., Pizzati, F., Elkins, K., Mukhopadhyay, S., Bibi, A., Csaba, B., Steibel, F., et al. (2024) · arXiv

Ethics and Social Impacts of AILaw, AI, and Intellectual Property

Cited by 2

Policy-Guided Diffusion

Jackson, M. T., Matthews, M. T., Lu, C., Ellis, B., Whiteson, S., & Foerster, J. (2024) · arXiv

Reinforcement Learning in RoboticsDomain Adaptation and Few-Shot Learning

Cited by 2

BALROG: Benchmarking Agentic LLM and VLM Reasoning On Games

Paglieri, D., Cupiał, B., Coward, S., Piterbarg, U., Wolczyk, M., Khan, A., Pignatelli, E., Kuciński, Ł., Pinto, L., Fergus, R., et al. (2024) · arXiv

Artificial Intelligence in GamesMultimodal Machine Learning Applications

Cited by 2

No Regrets: Investigating and Improving Regret Approximations for Curriculum Discovery

Rutherford, A., Beukman, M., Willi, T., Lacerda, B., Hawes, N., & Foerster, J. (2024) · arXiv

Reinforcement Learning in RoboticsAdvanced Bandit Algorithms Research

Cited by 2

Kinetix: Investigating the Training of General Agents through Open-Ended Physics-Based Control Tasks

Matthews, M., Beukman, M., Lu, C., & Foerster, J. (2024) · arXiv

Reinforcement Learning in RoboticsDomain Adaptation and Few-Shot Learning

Cited by 1

AIRA_2: Overcoming Bottlenecks in AI Research Agents

Hambardzumyan, K., Baldwin, N., Toledo, E., Hazra, R., Kuchnik, M., Omari, B. A., Foster, T. S., Protopopov, A., Gagnon-Audet, J.-C., Mediratta, I., et al. (2026) · arXiv

Artificial Intelligence ApplicationsScientific Computing and Data Management

Cited by 1

Recurrent Reinforcement Learning with Memoroids

Morad, S., Lu, C., Kortvelesy, R., Liwicki, S., Foerster, J., & Prorok, A. (2024) · arXiv

Reinforcement Learning in RoboticsNeural Networks and Applications

Cited by 1