Whiteson, Shimon

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

A Survey of Multi-Objective Sequential Decision-Making

Roijers, D. M., Vamplew, P., Whiteson, S., & Dazeley, R. (2013) · Journal of Artificial Intelligence Research

Reinforcement Learning in RoboticsAdvanced Multi-Objective Optimization Algorithms

Cited by 461

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

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

VariBAD: A Very Good Method for Bayes-Adaptive Deep RL via Meta-Learning

Zintgraf, L., Shiarlis, K., Igl, M., Schulze, S., Gal, Y., Hofmann, K., & Whiteson, S. (2019) · arXiv

Reinforcement Learning in RoboticsAdvanced Bandit Algorithms Research

Cited by 112

Protecting against evaluation overfitting in empirical reinforcement learning

Whiteson, S., Tanner, B., Taylor, M. E., & Stone, P. (2011) · 2011 IEEE Symposium on Adaptive Dynamic Programming and Reinforcement Learning (ADPRL)

Reinforcement Learning in RoboticsMachine Learning and Algorithms

Cited by 86

Transient Non-Stationarity and Generalisation in Deep Reinforcement Learning

Igl, M., Farquhar, G., Luketina, J., Boehmer, W., & Whiteson, S. (2020) · arXiv

Reinforcement Learning in RoboticsDomain Adaptation and Few-Shot Learning

Cited by 17

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

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

A Survey of In-Context Reinforcement Learning

Moeini, A., Wang, J., Beck, J., Blaser, E., Whiteson, S., Chandra, R., & Zhang, S. (2025) · arXiv

Reinforcement Learning in RoboticsDomain Adaptation and Few-Shot Learning

Cited by 0