Concurrent layered learning. - Shimon Whiteson; Peter Stone 001 (AAMAS, 2003)

Evolving Keepaway Soccer Players through Task Decomposition. - Shimon Whiteson; Nate Kohl; Risto Miikkulainen; Peter Stone 001 (GECCO, 2003)

Towards Autonomic Computing: Adaptive Job Routing and Scheduling. - Shimon Whiteson; Peter Stone 001 (AAAI, 2004)

Towards Autonomic Computing: Adaptive Network Routing and Scheduling. - Shimon Whiteson; Peter Stone 001 (ICAC, 2004)

Adaptive job routing and scheduling. - Shimon Whiteson; Peter Stone 001 (Eng. Appl. Artif. Intell., 2004)

Improving Reinforcement Learning Function Approximators via Neuroevolution. - Shimon Whiteson (AAAI, 2005)

Automatic feature selection in neuroevolution. - Shimon Whiteson; Peter Stone 001; Kenneth O. Stanley; Risto Miikkulainen; Nate Kohl (GECCO, 2005)

Evolving Soccer Keepaway Players Through Task Decomposition. - Shimon Whiteson; Nate Kohl; Risto Miikkulainen; Peter Stone 001 (Mach. Learn., 2005)

Sample-Efficient Evolutionary Function Approximation for Reinforcement Learning. - Shimon Whiteson; Peter Stone 001 (AAAI, 2006)

Comparing evolutionary and temporal difference methods in a reinforcement learning domain. - Matthew E. Taylor; Shimon Whiteson; Peter Stone 001 (GECCO, 2006)

On-line evolutionary computation for reinforcement learning in stochastic domains. - Shimon Whiteson; Peter Stone 001 (GECCO, 2006)

Evolutionary Function Approximation for Reinforcement Learning. - Shimon Whiteson; Peter Stone 001 (J. Mach. Learn. Res., 2006)

Temporal Difference and Policy Search Methods for Reinforcement Learning: An Empirical Comparison. - Matthew E. Taylor; Shimon Whiteson; Peter Stone 001 (AAAI, 2007)

Stochastic Optimization for Collision Selection in High Energy Physics. - Shimon Whiteson; Daniel Whiteson (AAAI, 2007)

Transfer via inter-task mappings in policy search reinforcement learning. - Matthew E. Taylor; Shimon Whiteson; Peter Stone 001 (AAMAS, 2007)

Empirical Studies in Action Selection with Reinforcement Learning. - Shimon Whiteson; Matthew E. Taylor; Peter Stone 001 (Adapt. Behav., 2007)

Exploiting locality of interaction in factored Dec-POMDPs. - Frans A. Oliehoek; Matthijs T. J. Spaan; Shimon Whiteson; Nikos Vlassis (AAMAS (1), 2008)

Multiagent Reinforcement Learning for Urban Traffic Control Using Coordination Graphs. - Lior Kuyer; Shimon Whiteson; Bram Bakker; Nikos Vlassis (ECML/PKDD (1), 2008)

A theoretical and empirical analysis of Expected Sarsa. - Harm van Seijen; Hado van Hasselt; Shimon Whiteson; Marco A. Wiering (ADPRL, 2009)

Lossless clustering of histories in decentralized POMDPs. - Frans A. Oliehoek; Shimon Whiteson; Matthijs T. J. Spaan (AAMAS (1), 2009)

Integrating distributed Bayesian inference and reinforcement learning for sensor management. - Corrado Grappiolo; Shimon Whiteson; Gregor Pavlin; Bram Bakker (FUSION, 2009)

Neuroevolutionary reinforcement learning for generalized helicopter control. - Rogier Koppejan; Shimon Whiteson (GECCO, 2009)

Automatic Feature Selection for Model-Based Reinforcement Learning in Factored MDPs. - Mark Kroon; Shimon Whiteson (ICMLA, 2009)

Postponed Updates for Temporal-Difference Reinforcement Learning. - Harm van Seijen; Shimon Whiteson (ISDA, 2009)

Machine learning for event selection in high energy physics. - Shimon Whiteson; Daniel Whiteson (Eng. Appl. Artif. Intell., 2009)

Multi-task evolutionary shaping without pre-specified representations. - Matthijs Snel; Shimon Whiteson (GECCO, 2010)

Critical factors in the empirical performance of temporal difference and evolutionary methods for reinforcement learning. - Shimon Whiteson; Matthew E. Taylor; Peter Stone 001 (Auton. Agents Multi Agent Syst., 2010)

Report on the 2008 Reinforcement Learning Competition. - Shimon Whiteson; Brian Tanner; Adam White 001 (AI Mag., 2010)

Adaptive Representations for Reinforcement Learning - Shimon Whiteson (Springer, 2010)

Traffic Light Control by Multiagent Reinforcement Learning Systems. - Bram Bakker; Shimon Whiteson; Leon J. H. M. Kester; Frans C. A. Groen (Interactive Collaborative Information Systems, 2010)

Switching between Representations in Reinforcement Learning. - Harm van Seijen; Shimon Whiteson; Leon J. H. M. Kester (Interactive Collaborative Information Systems, 2010)

Protecting against evaluation overfitting in empirical reinforcement learning. - Shimon Whiteson; Brian Tanner; Matthew E. Taylor; Peter Stone 001 (ADPRL, 2011)

A probabilistic method for inferring preferences from clicks. - Katja Hofmann; Shimon Whiteson; Maarten de Rijke (CIKM, 2011)

Balancing Exploration and Exploitation in Learning to Rank Online. - Katja Hofmann; Shimon Whiteson; Maarten de Rijke (ECIR, 2011)

Multi-Task Reinforcement Learning: Shaping and Feature Selection. - Matthijs Snel; Shimon Whiteson (EWRL, 2011)

Critical factors in the performance of novelty search. - Steijn Kistemaker; Shimon Whiteson (GECCO, 2011)

Robust central pattern generators for embodied hierarchical reinforcement learning. - Matthijs Snel; Shimon Whiteson; Yasuo Kuniyoshi (ICDL-EPIROB, 2011)

Adapting Rankers Online. - Katja Hofmann; Shimon Whiteson; Maarten de Rijke (IRFC, 2011)

Exploiting Agent and Type Independence in Collaborative Graphical Bayesian Games - Frans A. Oliehoek; Shimon Whiteson; Matthijs T. J. Spaan (CoRR, 2011)

Neuroevolutionary reinforcement learning for generalized control of simulated helicopters. - Rogier Koppejan; Shimon Whiteson (Evol. Intell., 2011)

Exploiting Best-Match Equations for Efficient Reinforcement Learning. - Harm van Seijen; Shimon Whiteson; Hado van Hasselt; Marco A. Wiering (J. Mach. Learn. Res., 2011)

Introduction to the special issue on empirical evaluations in reinforcement learning. - Shimon Whiteson; Michael L. Littman (Mach. Learn., 2011)

Evolutionary Computation for Reinforcement Learning. - Shimon Whiteson (Reinforcement Learning, 2012)

V-MAX: tempered optimism for better PAC reinforcement learning. - Karun Rao; Shimon Whiteson (AAMAS, 2012)

Estimating interleaved comparison outcomes from historical click data. - Katja Hofmann; Shimon Whiteson; Maarten de Rijke (CIKM, 2012)

Exploiting Structure in Cooperative Bayesian Games. - Frans A. Oliehoek; Shimon Whiteson; Matthijs T. J. Spaan (UAI, 2012)

Computing Convex Coverage Sets for Multi-objective Coordination Graphs. - Diederik M. Roijers; Shimon Whiteson; Frans A. Oliehoek (ADT, 2013)

Using informative behavior to increase engagement in the tamer framework. - Guangliang Li; Hayley Hung; Shimon Whiteson; W. Bradley Knox (AAMAS, 2013)

Approximate solutions for factored Dec-POMDPs with many agents. - Frans A. Oliehoek; Shimon Whiteson; Matthijs T. J. Spaan (AAMAS, 2013)

Multi-objective variable elimination for collaborative graphical games. - Diederik M. Roijers; Shimon Whiteson; Frans A. Oliehoek (AAMAS, 2013)

Lerot: an online learning to rank framework. - Anne Schuth; Katja Hofmann; Shimon Whiteson; Maarten de Rijke (LivingLab@CIKM, 2013)

Reusing Historical Interaction Data for Faster Online Learning to Rank for IR. - Katja Hofmann; Anne Schuth; Shimon Whiteson; Maarten de Rijke (DIR, 2013)

Critical factors in the performance of hyperNEAT. - Thomas G. van den Berg; Shimon Whiteson (GECCO, 2013)

Efficient Abstraction Selection in Reinforcement Learning (Extended Abstract). - Harm van Seijen; Shimon Whiteson; Leon J. H. M. Kester (SARA, 2013)

Balancing exploration and exploitation in listwise and pairwise online learning to rank for information retrieval. - Katja Hofmann; Shimon Whiteson; Maarten de Rijke (Inf. Retr., 2013)

Incremental Clustering and Expansion for Faster Optimal Planning in Dec-POMDPs. - Frans A. Oliehoek; Matthijs T. J. Spaan; Christopher Amato; Shimon Whiteson (J. Artif. Intell. Res., 2013)

A Survey of Multi-Objective Sequential Decision-Making. - Diederik M. Roijers; Peter Vamplew 001; Shimon Whiteson; Richard Dazeley (J. Artif. Intell. Res., 2013)

Fidelity, Soundness, and Efficiency of Interleaved Comparison Methods. - Katja Hofmann; Shimon Whiteson; Maarten de Rijke (ACM Trans. Inf. Syst., 2013)

Towards Personalised Gaming via Facial Expression Recognition. - Paris Mavromoustakos Blom; Sander Bakkes; Chek Tien Tan; Shimon Whiteson; Diederik M. Roijers; Roberto Valenti; Theo Gevers (AIIDE, 2014)

Bounded Approximations for Linear Multi-Objective Planning Under Uncertainty. - Diederik Marijn Roijers; Joris Scharpff; Matthijs T. J. Spaan; Frans A. Oliehoek; Mathijs de Weerdt; Shimon Whiteson (ICAPS, 2014)

Leveraging social networks to motivate humans to train agents. - Guangliang Li; Hayley Hung; Shimon Whiteson; W. Bradley Knox (AAMAS, 2014)

Linear support for multi-objective coordination graphs. - Diederik M. Roijers; Shimon Whiteson; Frans A. Oliehoek (AAMAS, 2014)

Multileaved Comparisons for Fast Online Evaluation. - Anne Schuth; Floor Sietsma; Shimon Whiteson; Damien Lefortier; Maarten de Rijke (CIKM, 2014)

Optimizing Base Rankers Using Clicks - A Case Study Using BM25. - Anne Schuth; Floor Sietsma; Shimon Whiteson; Maarten de Rijke (ECIR, 2014)

Design criteria for challenge balancing of personalised game spaces. - Sander Bakkes; Shimon Whiteson (FDG, 2014)

Challenge balancing for personalised game spaces. - Sander Bakkes; Shimon Whiteson; Guangliang Li; George Viorel Visniuc; Efstathios Charitos; Norbert Heijne; Arjen Swellengrebel (GEM, 2014)

Learning from human reward benefits from socio-competitive feedback. - Guangliang Li; Hayley Hung; Shimon Whiteson; W. Bradley Knox (ICDL-EPIROB, 2014)

Relative Upper Confidence Bound for the K-Armed Dueling Bandit Problem. - Masrour Zoghi; Shimon Whiteson; Rémi Munos; Maarten de Rijke (ICML, 2014)

Queued Pareto Local Search for Multi-Objective Optimization. - Maarten Inja; Chiel Kooijman; Maarten de Waard; Diederik M. Roijers; Shimon Whiteson (PPSN, 2014)

Relative confidence sampling for efficient on-line ranker evaluation. - Masrour Zoghi; Shimon Whiteson; Maarten de Rijke; Rémi Munos (WSDM, 2014)

Learning potential functions and their representations for multi-task reinforcement learning. - Matthijs Snel; Shimon Whiteson (Auton. Agents Multi Agent Syst., 2014)

Efficient Abstraction Selection in Reinforcement Learning. - Harm van Seijen; Shimon Whiteson; Leon J. H. M. Kester (Comput. Intell., 2014)

"Learning to rank for information retrieval from user interactions" by K. Hofmann, S. Whiteson, A. Schuth, and M. de Rijke with Martin Vesely as coordinator. - Katja Hofmann; Shimon Whiteson; Anne Schuth; Maarten de Rijke (SIGWEB Newsl., 2014)

Exploiting Submodular Value Functions for Faster Dynamic Sensor Selection. - Yash Satsangi; Shimon Whiteson; Frans A. Oliehoek (AAAI, 2015)

A Large-Scale Study of Agents Learning from Human Reward. - Guangliang Li; Hayley Hung; Shimon Whiteson (AAMAS, 2015)

Pareto Local Search for MOMDP Planning. - Chiel Kooijman; Maarten de Waard; Maarten Inja; Diederik M. Roijers; Shimon Whiteson (ESANN, 2015)

Point-Based Planning for Multi-Objective POMDPs. - Diederik Marijn Roijers; Shimon Whiteson; Frans A. Oliehoek (IJCAI, 2015)

Copeland Dueling Bandits. - Masrour Zoghi; Zohar S. Karnin; Shimon Whiteson; Maarten de Rijke (NIPS, 2015)

Bayesian Ranker Comparison Based on Historical User Interactions. - Artem Grotov; Shimon Whiteson; Maarten de Rijke (SIGIR, 2015)

MergeRUCB: A Method for Large-Scale Online Ranker Evaluation. - Masrour Zoghi; Shimon Whiteson; Maarten de Rijke (WSDM, 2015)

Computing Convex Coverage Sets for Faster Multi-objective Coordination. - Diederik Marijn Roijers; Shimon Whiteson; Frans A. Oliehoek (J. Artif. Intell. Res., 2015)

Towards Learning from Implicit Human Reward: (Extended Abstract). - Guangliang Li; Hamdi Dibeklioglu; Shimon Whiteson; Hayley Hung (AAMAS, 2016)

Probably Approximately Correct Greedy Maximization: (Extended Abstract). - Yash Satsangi; Shimon Whiteson; Frans A. Oliehoek (AAMAS, 2016)

Inverse Reinforcement Learning from Failure. - Kyriacos Shiarlis; João V. Messias; Shimon Whiteson (AAMAS, 2016)

PAC Greedy Maximization with Efficient Bounds on Information Gain for Sensor Selection. - Yash Satsangi; Shimon Whiteson; Frans A. Oliehoek (IJCAI, 2016)

Learning to Communicate with Deep Multi-Agent Reinforcement Learning. - Jakob N. Foerster; Yannis M. Assael; Nando de Freitas; Shimon Whiteson (NIPS, 2016)

Multileave Gradient Descent for Fast Online Learning to Rank. - Anne Schuth; Harrie Oosterhuis; Shimon Whiteson; Maarten de Rijke (WSDM, 2016)

Using informative behavior to increase engagement while learning from human reward. - Guangliang Li; Shimon Whiteson; W. Bradley Knox; Hayley Hung (Auton. Agents Multi Agent Syst., 2016)

LipNet: Sentence-level Lipreading. - Yannis M. Assael; Brendan Shillingford; Shimon Whiteson; Nando de Freitas (CoRR, 2016)

Learning to Communicate to Solve Riddles with Deep Distributed Recurrent Q-Networks. - Jakob N. Foerster; Yannis M. Assael; Nando de Freitas; Shimon Whiteson (CoRR, 2016)

Multi-Objective Deep Reinforcement Learning. - Hossam Mossalam; Yannis M. Assael; Diederik M. Roijers; Shimon Whiteson (CoRR, 2016)

Alternating Optimisation and Quadrature for Robust Reinforcement Learning. - Supratik Paul; Kamil Ciosek; Michael A. Osborne; Shimon Whiteson (CoRR, 2016)

Probably Approximately Correct Greedy Maximization. - Yash Satsangi; Shimon Whiteson; Frans A. Oliehoek (CoRR, 2016)

OFFER: Off-Environment Reinforcement Learning. - Kamil Andrzej Ciosek; Shimon Whiteson (AAAI, 2017)

Intro to Reinforcement Learning. - Shimon Whiteson (BMVC, 2017)

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)

Rapidly exploring learning trees. - Kyriacos Shiarlis; João V. Messias; Shimon Whiteson (ICRA, 2017)

Acquiring social interaction behaviours for telepresence robots via deep learning from demonstration. - Kyriacos Shiarlis; João V. Messias; Shimon Whiteson (IROS, 2017)

Dynamic-Depth Context Tree Weighting. - João V. Messias; Shimon Whiteson (NIPS, 2017)

Real-Time Resource Allocation for Tracking Systems. - Yash Satsangi; Shimon Whiteson; Frans A. Oliehoek; Henri Bouma (UAI, 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)

Multi-Objective Decision Making - Diederik M. Roijers; Shimon Whiteson (Morgan & Claypool Publishers, 2017)

Expected Policy Gradients. - Kamil Ciosek; Shimon Whiteson (AAAI, 2018)

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

Alternating Optimisation and Quadrature for Robust Control. - Supratik Paul; Konstantinos I. Chatzilygeroudis; Kamil Ciosek; Jean-Baptiste Mouret; Michael A. Osborne; Shimon Whiteson (AAAI, 2018)

Learning with Opponent-Learning Awareness. - Jakob N. Foerster; Richard Y. Chen; Maruan Al-Shedivat; Shimon Whiteson; Pieter Abbeel; Igor Mordatch (AAMAS, 2018)

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

Fourier Policy Gradients. - Matthew Fellows; Kamil Ciosek; Shimon Whiteson (ICML, 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)

Deep Variational Reinforcement Learning for POMDPs. - Maximilian Igl; Luisa M. Zintgraf; Tuan Anh Le 001; Frank Wood; 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)

TACO: Learning Task Decomposition via Temporal Alignment for Control. - Kyriacos Shiarlis; Markus Wulfmeier; Sasha Salter; Shimon Whiteson; Ingmar Posner (ICML, 2018)

Social interaction for efficient agent learning from human reward. - Guangliang Li; Shimon Whiteson; W. Bradley Knox; Hayley Hung (Auton. Agents Multi Agent Syst., 2018)

Exploiting submodular value functions for scaling up active perception. - Yash Satsangi; Shimon Whiteson; Frans A. Oliehoek; Matthijs T. J. Spaan (Auton. Robots, 2018)

Contextual Policy Optimisation. - Supratik Paul; Michael A. Osborne; Shimon Whiteson (CoRR, 2018)

CAML: Fast Context Adaptation via Meta-Learning. - Luisa M. Zintgraf; Kyriacos Shiarlis; Vitaly Kurin; Katja Hofmann; Shimon Whiteson (CoRR, 2018)

The Representational Capacity of Action-Value Networks for Multi-Agent Reinforcement Learning. - Jacopo Castellini; Frans A. Oliehoek; Rahul Savani; Shimon Whiteson (AAMAS, 2019)

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)

Stable Opponent Shaping in Differentiable Games. - Alistair Letcher; Jakob N. Foerster; David Balduzzi; Tim Rocktäschel; Shimon Whiteson (ICLR (Poster), 2019)

Bayesian Action Decoder for Deep Multi-Agent Reinforcement Learning. - Jakob N. Foerster; H. Francis Song; Edward Hughes 001; Neil Burch; Iain Dunning; Shimon Whiteson; Matthew M. Botvinick; Michael Bowling (ICML, 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)

Fingerprint Policy Optimisation for Robust Reinforcement Learning. - Supratik Paul; Michael A. Osborne; Shimon Whiteson (ICML, 2019)

Fast Context Adaptation via Meta-Learning. - Luisa M. Zintgraf; Kyriacos Shiarlis; Vitaly Kurin; Katja Hofmann; Shimon Whiteson (ICML, 2019)

Learning From Demonstration in the Wild. - Feryal M. P. Behbahani; Kyriacos Shiarlis; Xi Chen; Vitaly Kurin; Sudhanshu Kasewa; Ciprian Stirbu; João Gomes; Supratik Paul; Frans A. Oliehoek; João V. Messias; Shimon Whiteson (ICRA, 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)

VIREL: A Variational Inference Framework for Reinforcement Learning. - Matthew Fellows; Anuj Mahajan; Tim G. J. Rudner; Shimon Whiteson (NeurIPS, 2019)

MAVEN: Multi-Agent Variational Exploration. - Anuj Mahajan; Tabish Rashid; Mikayel Samvelyan; Shimon Whiteson (NeurIPS, 2019)

Fast Efficient Hyperparameter Tuning for Policy Gradient Methods. - Supratik Paul; Vitaly Kurin; Shimon Whiteson (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)

Generalized Off-Policy Actor-Critic. - Shangtong Zhang; Wendelin Boehmer; Shimon Whiteson (NeurIPS, 2019)

DAC: The Double Actor-Critic Architecture for Learning Options. - Shangtong Zhang; Shimon Whiteson (NeurIPS, 2019)

Fast Efficient Hyperparameter Tuning for Policy Gradients. - Supratik Paul; Vitaly Kurin; Shimon Whiteson (CoRR, 2019)

Multitask Soft Option Learning. - Maximilian Igl; Andrew Gambardella; Nantas Nardelli; N. Siddharth 001; Wendelin Böhmer; Shimon Whiteson (CoRR, 2019)

Exploration with Unreliable Intrinsic Reward in Multi-Agent Reinforcement Learning. - Wendelin Böhmer; Tabish Rashid; Shimon Whiteson (CoRR, 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)

Improving SAT Solver Heuristics with Graph Networks and Reinforcement Learning. - Vitaly Kurin; Saad Godil; Shimon Whiteson; Bryan Catanzaro (CoRR, 2019)

Provably Convergent Off-Policy Actor-Critic with Function Approximation. - Shangtong Zhang; Bo Liu 006; Hengshuai Yao; Shimon Whiteson (CoRR, 2019)

VIABLE: Fast Adaptation via Backpropagating Learned Loss. - Leo Feng; Luisa M. Zintgraf; Bei Peng 001; Shimon Whiteson (CoRR, 2019)

Maximizing Information Gain in Partially Observable Environments via Prediction Rewards. - Yash Satsangi; Sungsu Lim; Shimon Whiteson; Frans A. Oliehoek; Martha White (AAMAS, 2020)

Deep Residual Reinforcement Learning. - Shangtong Zhang; Wendelin Boehmer; Shimon Whiteson (AAMAS, 2020)

Optimistic Exploration even with a Pessimistic Initialisation. - Tabish Rashid; Bei Peng 001; Wendelin Boehmer; Shimon Whiteson (ICLR, 2020)

VariBAD: A Very Good Method for Bayes-Adaptive Deep RL via Meta-Learning. - Luisa M. Zintgraf; Kyriacos Shiarlis; Maximilian Igl; Sebastian Schulze; Yarin Gal; Katja Hofmann; Shimon Whiteson (ICLR, 2020)

Deep Coordination Graphs. - Wendelin Boehmer; Vitaly Kurin; Shimon Whiteson (ICML, 2020)

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

GradientDICE: Rethinking Generalized Offline Estimation of Stationary Values. - Shangtong Zhang; Bo Liu 006; Shimon Whiteson (ICML, 2020)

Provably Convergent Two-Timescale Off-Policy Actor-Critic with Function Approximation. - Shangtong Zhang; Bo Liu 006; Hengshuai Yao; Shimon Whiteson (ICML, 2020)

Can Q-Learning with Graph Networks Learn a Generalizable Branching Heuristic for a SAT Solver? - Vitaly Kurin; Saad Godil; Shimon Whiteson; Bryan Catanzaro (NeurIPS, 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)

Learning Retrospective Knowledge with Reverse Reinforcement Learning. - Shangtong Zhang; Vivek Veeriah; Shimon Whiteson (NeurIPS, 2020)

Multitask Soft Option Learning. - Maximilian Igl; Andrew Gambardella; Jinke He; Nantas Nardelli; N. Siddharth 001; Wendelin Boehmer; Shimon Whiteson (UAI, 2020)

Facial feedback for reinforcement learning: a case study and offline analysis using the TAMER framework. - Guangliang Li; Hamdi Dibeklioglu; Shimon Whiteson; Hayley Hung (Auton. Agents Multi Agent Syst., 2020)

Deep Multi-Agent Reinforcement Learning for Decentralized Continuous Cooperative Control. - Christian Schröder de Witt; Bei Peng 001; Pierre-Alexandre Kamienny; Philip H. S. Torr; Wendelin Böhmer; Shimon Whiteson (CoRR, 2020)

Per-Step Reward: A New Perspective for Risk-Averse Reinforcement Learning. - Shangtong Zhang; Bo Liu 006; Shimon Whiteson (CoRR, 2020)

Maximizing Information Gain in Partially Observable Environments via Prediction Reward. - Yash Satsangi; Sungsu Lim; Shimon Whiteson; Frans A. Oliehoek; Martha White (CoRR, 2020)

Privileged Information Dropout in Reinforcement Learning. - Pierre-Alexandre Kamienny; Kai Arulkumaran; Feryal M. P. Behbahani; Wendelin Boehmer; Shimon Whiteson (CoRR, 2020)

AI-QMIX: Attention and Imagination for Dynamic Multi-Agent Reinforcement Learning. - Shariq Iqbal; Christian A. Schröder de Witt; Bei Peng 001; Wendelin Böhmer; Shimon Whiteson; Fei Sha (CoRR, 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)

WordCraft: An Environment for Benchmarking Commonsense Agents. - Minqi Jiang; Jelena Luketina; Nantas Nardelli; Pasquale Minervini; Philip H. S. Torr; Shimon Whiteson; Tim Rocktäschel (CoRR, 2020)

Exploration in Approximate Hyper-State Space for Meta Reinforcement Learning. - Luisa M. Zintgraf; Leo Feng; Maximilian Igl; Kristian Hartikainen; Katja Hofmann; Shimon Whiteson (CoRR, 2020)

Is Independent Learning All You Need in the StarCraft Multi-Agent Challenge? - Christian Schröder de Witt; Tarun Gupta 002; Denys Makoviichuk; Viktor Makoviychuk; Philip H. S. Torr; Mingfei Sun 001; Shimon Whiteson (CoRR, 2020)

Expected Policy Gradients for Reinforcement Learning. - Kamil Ciosek; Shimon Whiteson (J. Mach. Learn. Res., 2020)

Robust Reinforcement Learning with Bayesian Optimisation and Quadrature. - Supratik Paul; Konstantinos I. Chatzilygeroudis; Kamil Ciosek; Jean-Baptiste Mouret; Michael A. Osborne; Shimon Whiteson (J. Mach. Learn. Res., 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)

Mean-Variance Policy Iteration for Risk-Averse Reinforcement Learning. - Shangtong Zhang; Bo Liu 006; Shimon Whiteson (AAAI, 2021)

Deep Interactive Bayesian Reinforcement Learning via Meta-Learning. - Luisa M. Zintgraf; Sam Devlin; Kamil Ciosek; Shimon Whiteson; Katja Hofmann (AAMAS, 2021)

RODE: Learning Roles to Decompose Multi-Agent Tasks. - Tonghan Wang 001; Tarun Gupta 002; Anuj Mahajan; Bei Peng 001; Shimon Whiteson; Chongjie Zhang (ICLR, 2021)

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

My Body is a Cage: the Role of Morphology in Graph-Based Incompatible Control. - Vitaly Kurin; Maximilian Igl; Tim Rocktäschel; Wendelin Boehmer; Shimon Whiteson (ICLR, 2021)

UneVEn: Universal Value Exploration for Multi-Agent Reinforcement Learning. - Tarun Gupta 002; Anuj Mahajan; Bei Peng 001; Wendelin Boehmer; Shimon Whiteson (ICML, 2021)

Randomized Entity-wise Factorization for Multi-Agent Reinforcement Learning. - Shariq Iqbal; Christian A. Schröder de Witt; Bei Peng 001; Wendelin Boehmer; Shimon Whiteson; Fei Sha (ICML, 2021)

Tesseract: Tensorised Actors for Multi-Agent Reinforcement Learning. - Anuj Mahajan; Mikayel Samvelyan; Lei Mao; Viktor Makoviychuk; Animesh Garg; Jean Kossaifi; Shimon Whiteson; Yuke Zhu; Animashree Anandkumar (ICML, 2021)

Average-Reward Off-Policy Policy Evaluation with Function Approximation. - Shangtong Zhang; Yi Wan 004; Richard S. Sutton; Shimon Whiteson (ICML, 2021)

Breaking the Deadly Triad with a Target Network. - Shangtong Zhang; Hengshuai Yao; Shimon Whiteson (ICML, 2021)

Exploration in Approximate Hyper-State Space for Meta Reinforcement Learning. - Luisa M. Zintgraf; Leo Feng; Cong Lu; Maximilian Igl; Kristian Hartikainen; Katja Hofmann; Shimon Whiteson (ICML, 2021)

Deep Residual Reinforcement Learning (Extended Abstract). - Shangtong Zhang; Wendelin Boehmer; Shimon Whiteson (IJCAI, 2021)

Snowflake: Scaling GNNs to high-dimensional continuous control via parameter freezing. - Charlie Blake; Vitaly Kurin; Maximilian Igl; Shimon Whiteson (NeurIPS, 2021)

Bayesian Bellman Operators. - Mattie Fellows; Kristian Hartikainen; Shimon Whiteson (NeurIPS, 2021)

Regularized Softmax Deep Multi-Agent Q-Learning. - Ling Pan; Tabish Rashid; Bei Peng 001; Longbo Huang; Shimon Whiteson (NeurIPS, 2021)

FACMAC: Factored Multi-Agent Centralised Policy Gradients. - Bei Peng 001; Tabish Rashid; Christian Schröder de Witt; Pierre-Alexandre Kamienny; Philip H. S. Torr; Wendelin Boehmer; Shimon Whiteson (NeurIPS, 2021)

Analysing factorizations of action-value networks for cooperative multi-agent reinforcement learning. - Jacopo Castellini; Frans A. Oliehoek; Rahul Savani; Shimon Whiteson (Auton. Agents Multi Agent Syst., 2021)

Softmax with Regularization: Better Value Estimation in Multi-Agent Reinforcement Learning. - Ling Pan; Tabish Rashid; Bei Peng 001; Longbo Huang; Shimon Whiteson (CoRR, 2021)

Semi-On-Policy Training for Sample Efficient Multi-Agent Policy Gradients. - Bozhidar Vasilev; Tarun Gupta 002; Bei Peng 001; Shimon Whiteson (CoRR, 2021)

SoftDICE for Imitation Learning: Rethinking Off-policy Distribution Matching. - Mingfei Sun 001; Anuj Mahajan; Katja Hofmann; Shimon Whiteson (CoRR, 2021)

Bayesian Bellman Operators. - Matthew Fellows; Kristian Hartikainen; Shimon Whiteson (CoRR, 2021)

Implicit Communication as Minimum Entropy Coupling. - Samuel Sokota; Christian Schröder de Witt; Maximilian Igl; Luisa M. Zintgraf; Philip H. S. Torr; Shimon Whiteson; Jakob N. Foerster (CoRR, 2021)

Model based Multi-agent Reinforcement Learning with Tensor Decompositions. - Pascal Van Der Vaart; Anuj Mahajan; Shimon Whiteson (CoRR, 2021)

Reinforcement Learning in Factored Action Spaces using Tensor Decompositions. - Anuj Mahajan; Mikayel Samvelyan; Lei Mao; Viktor Makoviychuk; Animesh Garg; Jean Kossaifi; Shimon Whiteson; Yuke Zhu; Animashree Anandkumar (CoRR, 2021)

On the Practical Consistency of Meta-Reinforcement Learning Algorithms. - Zheng Xiong; Luisa M. Zintgraf; Jacob Beck; Risto Vuorio; Shimon Whiteson (CoRR, 2021)

VariBAD: Variational Bayes-Adaptive Deep RL via Meta-Learning. - Luisa M. Zintgraf; Sebastian Schulze; Cong Lu; Leo Feng; Maximilian Igl; Kyriacos Shiarlis; Yarin Gal; Katja Hofmann; Shimon Whiteson (J. Mach. Learn. Res., 2021)

Reinforcement learning enhanced quantum-inspired algorithm for combinatorial optimization. - Dmitrii Beloborodov; Alexander E. Ulanov; Jakob N. Foerster; Shimon Whiteson; A. I. Lvovsky 001 (Mach. Learn. Sci. Technol., 2021)

Deterministic and Discriminative Imitation (D2-Imitation): Revisiting Adversarial Imitation for Sample Efficiency. - Mingfei Sun 001; Sam Devlin; Katja Hofmann; Shimon Whiteson (AAAI, 2022)

A Deeper Look at Discounting Mismatch in Actor-Critic Algorithms. - Shangtong Zhang; Romain Laroche; Harm van Seijen; Shimon Whiteson; Remi Tachet des Combes (AAMAS, 2022)

Learning Skills Diverse in Value-Relevant Features. - Matthew J. A. Smith; Jelena Luketina; Kristian Hartikainen; Maximilian Igl; Shimon Whiteson (CoLLAs, 2022)

Hypernetworks in Meta-Reinforcement Learning. - Jacob Beck; Matthew Thomas Jackson; Risto Vuorio; Shimon Whiteson (CoRL, 2022)

Embedding Synthetic Off-Policy Experience for Autonomous Driving via Zero-Shot Curricula. - Eli Bronstein; Sirish Srinivasan; Supratik Paul; Aman Sinha 001; Matthew O'Kelly; Payam Nikdel; Shimon Whiteson (CoRL, 2022)

Particle-Based Score Estimation for State Space Model Learning in Autonomous Driving. - Angad Singh; Omar Makhlouf; Maximilian Igl; João V. Messias; Arnaud Doucet; Shimon Whiteson (CoRL, 2022)

Generalized Beliefs for Cooperative AI. - Darius Muglich; Luisa M. Zintgraf; Christian A. Schröder de Witt; Shimon Whiteson; Jakob N. Foerster (ICML, 2022)

Communicating via Markov Decision Processes. - Samuel Sokota; Christian A. Schröder de Witt; Maximilian Igl; Luisa M. Zintgraf; Philip H. S. Torr; Martin Strohmeier; J. Zico Kolter; Shimon Whiteson; Jakob N. Foerster (ICML, 2022)

Symphony: Learning Realistic and Diverse Agents for Autonomous Driving Simulation. - Maximilian Igl; Daewoo Kim; Alex Kuefler; Paul Mougin; Punit Shah; Kyriacos Shiarlis; Dragomir Anguelov; Mark Palatucci; Brandyn White; Shimon Whiteson (ICRA, 2022)

Hierarchical Model-Based Imitation Learning for Planning in Autonomous Driving. - Eli Bronstein; Mark Palatucci; Dominik Notz; Brandyn White; Alex Kuefler; Yiren Lu 001; Supratik Paul; Payam Nikdel; Paul Mougin; Hongge Chen; Justin Fu; Austin Abrams; et al. (IROS, 2022)

In Defense of the Unitary Scalarization for Deep Multi-Task Learning. - Vitaly Kurin; Alessandro De Palma; Ilya Kostrikov; Shimon Whiteson; Pawan Kumar Mudigonda (NeurIPS, 2022)

Equivariant Networks for Zero-Shot Coordination. - Darius Muglich; Christian Schröder de Witt; Elise van der Pol; Shimon Whiteson; Jakob N. Foerster (NeurIPS, 2022)

In Defense of the Unitary Scalarization for Deep Multi-Task Learning. - Vitaly Kurin; Alessandro De Palma; Ilya Kostrikov; Shimon Whiteson; M. Pawan Kumar (CoRR, 2022)

You May Not Need Ratio Clipping in PPO. - Mingfei Sun 001; Vitaly Kurin; Guoqing Liu; Sam Devlin; Tao Qin 001; Katja Hofmann; Shimon Whiteson (CoRR, 2022)

Monotonic Improvement Guarantees under Non-stationarity for Decentralized PPO. - Mingfei Sun 001; Sam Devlin; Katja Hofmann; Shimon Whiteson (CoRR, 2022)

Generalization in Cooperative Multi-Agent Systems. - Anuj Mahajan; Mikayel Samvelyan; Tarun Gupta 002; Benjamin Ellis; Mingfei Sun 001; Tim Rocktäschel; Shimon Whiteson (CoRR, 2022)

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

SMACv2: An Improved Benchmark for Cooperative Multi-Agent Reinforcement Learning. - Benjamin Ellis; Skander Moalla; Mikayel Samvelyan; Mingfei Sun 001; Anuj Mahajan; Jakob N. Foerster; Shimon Whiteson (CoRR, 2022)

Truncated Emphatic Temporal Difference Methods for Prediction and Control. - Shangtong Zhang; Shimon Whiteson (J. Mach. Learn. Res., 2022)

Trust Region Bounds for Decentralized PPO Under Non-stationarity. - Mingfei Sun 001; Sam Devlin; Jacob Beck; Katja Hofmann; Shimon Whiteson (AAMAS, 2023)

Cheap Talk Discovery and Utilization in Multi-Agent Reinforcement Learning. - Yat Long Lo; Christian Schröder de Witt; Samuel Sokota; Jakob Nicolaus Foerster; Shimon Whiteson (ICLR, 2023)

Why Target Networks Stabilise Temporal Difference Methods. - Mattie Fellows; Matthew J. A. Smith; Shimon Whiteson (ICML, 2023)

Universal Morphology Control via Contextual Modulation. - Zheng Xiong; Jacob Beck; Shimon Whiteson (ICML, 2023)

Hierarchical Imitation Learning for Stochastic Environments. - Maximilian Igl; Punit Shah; Paul Mougin; Sirish Srinivasan; Tarun Gupta 002; Brandyn White; Kyriacos Shiarlis; Shimon Whiteson (IROS, 2023)

Imitation Is Not Enough: Robustifying Imitation with Reinforcement Learning for Challenging Driving Scenarios. - Yiren Lu 001; Justin Fu; George Tucker; Xinlei Pan; Eli Bronstein; Rebecca Roelofs; Benjamin Sapp; Brandyn White; Aleksandra Faust; Shimon Whiteson; Dragomir Anguelov; Sergey Levine (IROS, 2023)

Recurrent Hypernetworks are Surprisingly Strong in Meta-RL. - Jacob Beck; Risto Vuorio; Zheng Xiong; Shimon Whiteson (NeurIPS, 2023)

SMACv2: An Improved Benchmark for Cooperative Multi-Agent Reinforcement Learning. - Benjamin Ellis; Jonathan Cook 004; Skander Moalla; Mikayel Samvelyan; Mingfei Sun 001; Anuj Mahajan; Jakob N. Foerster; Shimon Whiteson (NeurIPS, 2023)

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)

The Waymo Open Sim Agents Challenge. - Nico Montali; John Lambert; Paul Mougin; Alex Kuefler; Nicholas Rhinehart; Michelle Li; Cole Gulino; Tristan Emrich; Zoey Yang; Shimon Whiteson; Brandyn White; Dragomir Anguelov (NeurIPS, 2023)

A Survey of Meta-Reinforcement Learning. - Jacob Beck; Risto Vuorio; Evan Zheran Liu; Zheng Xiong; Luisa M. Zintgraf; Chelsea Finn; Shimon Whiteson (CoRR, 2023)

Trust-Region-Free Policy Optimization for Stochastic Policies. - Mingfei Sun 001; Benjamin Ellis; Anuj Mahajan; Sam Devlin; Katja Hofmann; Shimon Whiteson (CoRR, 2023)

JaxMARL: Multi-Agent RL Environments in JAX. - Alexander Rutherford; Benjamin Ellis; Matteo Gallici; Jonathan Cook 004; Andrei Lupu; Garðar Ingvarsson; Timon Willi; Akbir Khan; Christian Schröder de Witt; Alexandra Souly; Saptarashmi Bandyopadhyay; Mikayel Samvelyan; et al. (CoRR, 2023)

JaxMARL: Multi-Agent RL Environments and Algorithms in JAX. - Alexander Rutherford; Benjamin Ellis; Matteo Gallici; Jonathan Cook 004; Andrei Lupu; Garðar Ingvarsson; Timon Willi; Akbir Khan; Christian Schröder de Witt; Alexandra Souly; Saptarashmi Bandyopadhyay; Mikayel Samvelyan; et al. (AAMAS, 2024)

Rate-Informed Discovery via Bayesian Adaptive Multifidelity Sampling. - Aman Sinha 001; Payam Nikdel; Supratik Paul; Shimon Whiteson (CoRL, 2024)

Discovering Temporally-Aware Reinforcement Learning Algorithms. - Matthew Thomas Jackson; Chris Lu 001; Louis Kirsch; Robert Tjarko Lange; Shimon Whiteson; Jakob Nicolaus Foerster (ICLR, 2024)

Bayesian Exploration Networks. - Mattie Fellows; Brandon Kaplowitz; Christian Schröder de Witt; Shimon Whiteson (ICML, 2024)

Distilling Morphology-Conditioned Hypernetworks for Efficient Universal Morphology Control. - Zheng Xiong; Risto Vuorio; Jacob Beck; Matthieu Zimmer; Kun Shao; Shimon Whiteson (ICML, 2024)

UniGen: Unified Modeling of Initial Agent States and Trajectories for Generating Autonomous Driving Scenarios. - Reza Mahjourian; Rongbing Mu; Valerii Likhosherstov; Paul Mougin; Xiukun Huang; João V. Messias; Shimon Whiteson (ICRA, 2024)

Adam on Local Time: Addressing Nonstationarity in RL with Relative Adam Timesteps. - Benjamin Ellis; Matthew Thomas Jackson; Andrei Lupu; Alexander David Goldie; Mattie Fellows; Shimon Whiteson; Jakob N. Foerster (NeurIPS, 2024)

Can Learned Optimization Make Reinforcement Learning Less Difficult? - Alexander David Goldie; Chris Lu 001; Matthew Thomas Jackson; Shimon Whiteson; Jakob N. Foerster (NeurIPS, 2024)

JaxMARL: Multi-Agent RL Environments and Algorithms in JAX. - Alexander Rutherford; Benjamin Ellis; Matteo Gallici; Jonathan Cook 004; Andrei Lupu; Garðar Ingvarsson; Timon Willi; Ravi Hammond; Akbir Khan; Christian Schröder de Witt; Alexandra Souly; Saptarashmi Bandyopadhyay; et al. (NeurIPS, 2024)

SplAgger: Split Aggregation for Meta-Reinforcement Learning. - Jacob Beck; Matthew Thomas Jackson; Risto Vuorio; Zheng Xiong; Shimon Whiteson (RLJ, 2024)

Policy-Guided Diffusion. - Matthew Thomas Jackson; Michael T. Matthews; Cong Lu; Benjamin Ellis; Shimon Whiteson; Jakob Nicolaus Foerster (RLJ, 2024)

A Bayesian Solution To The Imitation Gap. - Risto Vuorio; Mattie Fellows; Cong Lu; Clémence Grislain; Shimon Whiteson (CoRR, 2024)

IGDrivSim: A Benchmark for the Imitation Gap in Autonomous Driving. - Clémence Grislain; Risto Vuorio; Cong Lu; Shimon Whiteson (CoRR, 2024)

A Tutorial on Meta-Reinforcement Learning. - Jacob Beck; Risto Vuorio; Evan Zheran Liu; Zheng Xiong; Luisa M. Zintgraf; Chelsea Finn; Shimon Whiteson (Found. Trends Mach. Learn., 2025)

A Survey of In-Context Reinforcement Learning. - Amir Moeini; Jiuqi Wang; Jacob Beck; Ethan Blaser; Shimon Whiteson; Rohan Chandra; Shangtong Zhang (CoRR, 2025)

A Clean Slate for Offline Reinforcement Learning. - Matthew Thomas Jackson; Uljad Berdica; Jarek Liesen; Shimon Whiteson; Jakob Nicolaus Foerster (CoRR, 2025)

GoalLadder: Incremental Goal Discovery with Vision-Language Models. - Alexey Zakharov; Shimon Whiteson (CoRR, 2025)

How Should We Meta-Learn Reinforcement Learning Algorithms? - Alexander David Goldie; Zilin Wang; Jakob Nicolaus Foerster; Shimon Whiteson (CoRR, 2025)