Stabilising Experience Replay for Deep Multi-Agent Reinforcement Learning. - Jakob N. Foerster; Nantas Nardelli; Gregory Farquhar; Triantafyllos Afouras; Philip H. S. Torr; Pushmeet Kohli; Shimon Whiteson (ICML, 2017)

Stabilising Experience Replay for Deep Multi-Agent Reinforcement Learning. - Jakob N. Foerster; Nantas Nardelli; Gregory Farquhar; Philip H. S. Torr; Pushmeet Kohli; Shimon Whiteson (CoRR, 2017)

TreeQN and ATreeC: Differentiable Tree Planning for Deep Reinforcement Learning. - Gregory Farquhar; Tim Rocktäschel; Maximilian Igl; Shimon Whiteson (CoRR, 2017)

Counterfactual Multi-Agent Policy Gradients. - Jakob N. Foerster; Gregory Farquhar; Triantafyllos Afouras; Nantas Nardelli; Shimon Whiteson (AAAI, 2018)

TreeQN and ATreeC: Differentiable Tree-Structured Models for Deep Reinforcement Learning. - Gregory Farquhar; Tim Rocktäschel; Maximilian Igl; Shimon Whiteson (ICLR (Poster), 2018)

DiCE: The Infinitely Differentiable Monte Carlo Estimator. - Jakob N. Foerster; Gregory Farquhar; Maruan Al-Shedivat; Tim Rocktäschel; Eric P. Xing; Shimon Whiteson (ICML, 2018)

QMIX: Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learning. - Tabish Rashid; Mikayel Samvelyan; Christian Schröder de Witt; Gregory Farquhar; Jakob N. Foerster; Shimon Whiteson (ICML, 2018)

The StarCraft Multi-Agent Challenge. - Mikayel Samvelyan; Tabish Rashid; Christian Schröder de Witt; Gregory Farquhar; Nantas Nardelli; Tim G. J. Rudner; Chia-Man Hung; Philip H. S. Torr; Jakob N. Foerster; Shimon Whiteson (AAMAS, 2019)

A Baseline for Any Order Gradient Estimation in Stochastic Computation Graphs. - Jingkai Mao; Jakob N. Foerster; Tim Rocktäschel; Maruan Al-Shedivat; Gregory Farquhar; Shimon Whiteson (ICML, 2019)

A Survey of Reinforcement Learning Informed by Natural Language. - Jelena Luketina; Nantas Nardelli; Gregory Farquhar; Jakob N. Foerster; Jacob Andreas; Edward Grefenstette; Shimon Whiteson; Tim Rocktäschel (IJCAI, 2019)

Loaded DiCE: Trading off Bias and Variance in Any-Order Score Function Gradient Estimators for Reinforcement Learning. - Gregory Farquhar; Shimon Whiteson; Jakob N. Foerster (NeurIPS, 2019)

Multi-Agent Common Knowledge Reinforcement Learning. - Christian Schröder de Witt; Jakob N. Foerster; Gregory Farquhar; Philip H. S. Torr; Wendelin Boehmer; Shimon Whiteson (NeurIPS, 2019)

Loaded DiCE: Trading off Bias and Variance in Any-Order Score Function Estimators for Reinforcement Learning. - Gregory Farquhar; Shimon Whiteson; Jakob N. Foerster (CoRR, 2019)

Growing Action Spaces. - Gregory Farquhar; Laura Gustafson; Zeming Lin; Shimon Whiteson; Nicolas Usunier; Gabriel Synnaeve (ICML, 2020)

Weighted QMIX: Expanding Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learning. - Tabish Rashid; Gregory Farquhar; Bei Peng 001; Shimon Whiteson (NeurIPS, 2020)

The Impact of Non-stationarity on Generalisation in Deep Reinforcement Learning. - Maximilian Igl; Gregory Farquhar; Jelena Luketina; Wendelin Boehmer; Shimon Whiteson (CoRR, 2020)

Weighted QMIX: Expanding Monotonic Value Function Factorisation. - Tabish Rashid; Gregory Farquhar; Bei Peng 001; Shimon Whiteson (CoRR, 2020)

Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learning. - Tabish Rashid; Mikayel Samvelyan; Christian Schröder de Witt; Gregory Farquhar; Jakob N. Foerster; Shimon Whiteson (J. Mach. Learn. Res., 2020)

Transient Non-stationarity and Generalisation in Deep Reinforcement Learning. - Maximilian Igl; Gregory Farquhar; Jelena Luketina; Wendelin Boehmer; Shimon Whiteson (ICLR, 2021)

PsiPhi-Learning: Reinforcement Learning with Demonstrations using Successor Features and Inverse Temporal Difference Learning. - Angelos Filos; Clare Lyle; Yarin Gal; Sergey Levine; Natasha Jaques; Gregory Farquhar (ICML, 2021)

Self-Consistent Models and Values. - Gregory Farquhar; Kate Baumli; Zita Marinho; Angelos Filos; Matteo Hessel; Hado Philip van Hasselt; David Silver (NeurIPS, 2021)

Proper Value Equivalence. - Christopher Grimm; André Barreto 001; Gregory Farquhar; David Silver; Satinder Singh 001 (NeurIPS, 2021)

Model-Value Inconsistency as a Signal for Epistemic Uncertainty. - Angelos Filos; Eszter Vértes; Zita Marinho; Gregory Farquhar; Diana Borsa; Abram L. Friesen; Feryal M. P. Behbahani; Tom Schaul; André Barreto 001; Simon Osindero (ICML, 2022)

An Investigation of the Bias-Variance Tradeoff in Meta-Gradients. - Risto Vuorio; Jacob Beck; Shimon Whiteson; Jakob N. Foerster; Gregory Farquhar (CoRR, 2022)

Discovering General Reinforcement Learning Algorithms with Adversarial Environment Design. - Matthew Thomas Jackson; Minqi Jiang; Jack Parker-Holder; Risto Vuorio; Chris Lu 001; Gregory Farquhar; Shimon Whiteson; Jakob N. Foerster (NeurIPS, 2023)

Scalable Meta-Learning via Mixed-Mode Differentiation. - Iurii Kemaev; Dan A. Calian; Luisa M. Zintgraf; Gregory Farquhar; Hado van Hasselt (CoRR, 2025)

DataRater: Meta-Learned Dataset Curation. - Dan A. Calian; Gregory Farquhar; Iurii Kemaev; Luisa M. Zintgraf; Matteo Hessel; Jeremy Shar; Junhyuk Oh; András György 001; Tom Schaul; Jeffrey Dean; Hado van Hasselt; David Silver (CoRR, 2025)

The 2025 PNPL Competition: Speech Detection and Phoneme Classification in the LibriBrain Dataset. - Gilad Landau; Miran Özdogan; Gereon Elvers; Francesco Mantegna; Pratik Somaiya; Dulhan Jayalath; Luisa Kurth; Teyun Kwon; Brendan Shillingford; Gregory Farquhar; Minqi Jiang; Karim Jerbi; et al. (CoRR, 2025)