Shimon Whiteson : Publications

Books

• [1]

  Multi−Objective Decision Making

  Diederik Roijers and Shimon Whiteson

  Morgan and Claypool, California‚ USA. 2017.

  doi:10.2200/S00765ED1V01Y201704AIM034

  Details about Multi−Objective Decision Making | BibTeX data for Multi−Objective Decision Making | Link to Multi−Objective Decision Making

• [2]

  Adaptive Representations for Reinforcement Learning

  Shimon Whiteson

  Vol. 291 of Studies in Computational Intelligence. Springer, Berlin‚ Germany. 2010.

  Details about Adaptive Representations for Reinforcement Learning | BibTeX data for Adaptive Representations for Reinforcement Learning | Download (pdf) of Adaptive Representations for Reinforcement Learning

Book chapters

• [1]

  Evolutionary Computation for Reinforcement Learning

  Shimon Whiteson

  In Marco Wiering and Martijn van Otterlo, editors, Reinforcement Learning: State of the Art. Pages 325−355. Springer, Berlin‚ Germany. 2012.

  Details about Evolutionary Computation for Reinforcement Learning | BibTeX data for Evolutionary Computation for Reinforcement Learning | Download (pdf) of Evolutionary Computation for Reinforcement Learning

• [2]

  Evolutionary Computation for Reinforcement Learning

  Shimon Whiteson

  In Marco Wiering and Martijn van Otterlo, editors, Reinforcement Learning: State of the Art. Pages 325−355. Springer, Berlin‚ Germany. 2012.

  Details about Evolutionary Computation for Reinforcement Learning | BibTeX data for Evolutionary Computation for Reinforcement Learning | Download (pdf) of Evolutionary Computation for Reinforcement Learning

• [3]

  Traffic Light Control by Multiagent Reinforcement Learning Systems

  Bram Bakker‚ Shimon Whiteson‚ Leon Kester and Frans Groen

  In Robert Babuska and Frans Groen, editors, Interactive Collaborative Information Systems. Pages 475−510. Springer, Berlin‚ Germany. 2010.

  Details about Traffic Light Control by Multiagent Reinforcement Learning Systems | BibTeX data for Traffic Light Control by Multiagent Reinforcement Learning Systems | Download (pdf) of Traffic Light Control by Multiagent Reinforcement Learning Systems

• [4]

  Switching between Representations in Reinforcement Learning

  Harm van Seijen‚ Shimon Whiteson and Leon Kester

  In Robert Babuska and Frans Groen, editors, Interactive Collaborative Information Systems. Pages 65−84. Springer, Berlin‚ Germany. 2010.

  Details about Switching between Representations in Reinforcement Learning | BibTeX data for Switching between Representations in Reinforcement Learning | Download (pdf) of Switching between Representations in Reinforcement Learning

• [5]

  Traffic Light Control by Multiagent Reinforcement Learning Systems

  Bram Bakker‚ Shimon Whiteson‚ Leon Kester and Frans Groen

  In Robert Babuska and Frans Groen, editors, Interactive Collaborative Information Systems. Pages 475−510. Springer, Berlin‚ Germany. 2010.

  Details about Traffic Light Control by Multiagent Reinforcement Learning Systems | BibTeX data for Traffic Light Control by Multiagent Reinforcement Learning Systems | Download (pdf) of Traffic Light Control by Multiagent Reinforcement Learning Systems

• [6]

  Switching between Representations in Reinforcement Learning

  Harm van Seijen‚ Shimon Whiteson and Leon Kester

  In Robert Babuska and Frans Groen, editors, Interactive Collaborative Information Systems. Pages 65−84. Springer, Berlin‚ Germany. 2010.

  Details about Switching between Representations in Reinforcement Learning | BibTeX data for Switching between Representations in Reinforcement Learning | Download (pdf) of Switching between Representations in Reinforcement Learning

Journal papers

• [1]

  Truncated Emphatic Temporal Difference Methods for Prediction and Control

  Shangtong Zhang and Shimon Whiteson

  In Journal of Machine Learning Research. Vol. 23. No. 153. Pages 1–59. 2022.

  Details about Truncated Emphatic Temporal Difference Methods for Prediction and Control | BibTeX data for Truncated Emphatic Temporal Difference Methods for Prediction and Control | Link to Truncated Emphatic Temporal Difference Methods for Prediction and Control

• [2]

  VariBAD: Variational Bayes−Adaptive Deep RL via Meta−Learning

  Luisa Zintgraf‚ Sebastian Schulze‚ Cong Lu‚ Leo Feng‚ Maximilian Igl‚ Kyriacos Shiarlis‚ Yarin Gal‚ Katja Hofmann and Shimon Whiteson

  In Journal of Machine Learning Research. Vol. 22(289). Pages 1−39. 2021.

  Details about VariBAD: Variational Bayes−Adaptive Deep RL via Meta−Learning | BibTeX data for VariBAD: Variational Bayes−Adaptive Deep RL via Meta−Learning | Download (pdf) of VariBAD: Variational Bayes−Adaptive Deep RL via Meta−Learning

• [3]

  Analysing factorizations of action−value networks for cooperative multi−agent reinforcement learning

  Jacopo Castellini‚ Frans Oliehoek‚ Rahul Savani and Shimon Whiteson

  In Autonomous Agents and Multi−Agent Systems. Vol. 35. No. 2. 2021.

  Details about Analysing factorizations of action−value networks for cooperative multi−agent reinforcement learning | BibTeX data for Analysing factorizations of action−value networks for cooperative multi−agent reinforcement learning | Download (pdf) of Analysing factorizations of action−value networks for cooperative multi−agent reinforcement learning

• [4]

  Monotonic Value Function Factorisation for Deep Multi−Agent Reinforcement Learning

  Tabish Rashid‚ Mikayel Samvelyan‚ Christian Schroeder de Witt‚ Gregory Farquhar‚ Jakob Foerster and Shimon Whiteson

  In Journal of Machine Learning Research. Vol. 21(178). Pages 1−51. 2020.

  Details about Monotonic Value Function Factorisation for Deep Multi−Agent Reinforcement Learning | BibTeX data for Monotonic Value Function Factorisation for Deep Multi−Agent Reinforcement Learning | Download (pdf) of Monotonic Value Function Factorisation for Deep Multi−Agent Reinforcement Learning

• [5]

  Expected Policy Gradients for Reinforcement Learning

  Kamil Ciosek and Shimon Whiteson

  In Journal of Machine Learning Research. Vol. 21(52). Pages 1−51. 2020.

  Details about Expected Policy Gradients for Reinforcement Learning | BibTeX data for Expected Policy Gradients for Reinforcement Learning | Download (pdf) of Expected Policy Gradients for Reinforcement Learning

• [6]

  Social Interaction for Efficient Agent Learning from Human Reward

  Guangliang Li‚ Shimon Whiteson‚ W. Bradley Knox and Hayley Hung

  In Autonomous Agents and Multi−Agent Systems. Vol. 32. No. 1. Pages 1−25. 2018.

  Details about Social Interaction for Efficient Agent Learning from Human Reward | BibTeX data for Social Interaction for Efficient Agent Learning from Human Reward | Link to Social Interaction for Efficient Agent Learning from Human Reward

• [7]

  Exploiting submodular value functions for scaling up active perception

  Yash Satsangi‚ Shimon Whiteson‚ Frans Oliehoek and Matthijs Spaan

  In Autonomous Robots. 2017.

  Details about Exploiting submodular value functions for scaling up active perception | BibTeX data for Exploiting submodular value functions for scaling up active perception | Download (pdf) of Exploiting submodular value functions for scaling up active perception

• [8]

  Social Interaction for Efficient Agent Learning from Human Reward

  Guangliang Li‚ Shimon Whiteson‚ W. Bradley Knox and Hayley Hung

  In Autonomous Agents and Multi−Agent Systems. 2017.

  To appear.

  Details about Social Interaction for Efficient Agent Learning from Human Reward | BibTeX data for Social Interaction for Efficient Agent Learning from Human Reward | Link to Social Interaction for Efficient Agent Learning from Human Reward

• [9]

  Using Informative Behavior to Increase Engagement while Learning from Human Reward

  Guangliang Li‚ Shimon Whiteson‚ W. Bradley Knox and Hayley Hung

  In Autonomous Agents and Multi−Agent Systems. Vol. 30. No. 5. Pages 826−848. 2016.

  Details about Using Informative Behavior to Increase Engagement while Learning from Human Reward | BibTeX data for Using Informative Behavior to Increase Engagement while Learning from Human Reward | Download (pdf) of Using Informative Behavior to Increase Engagement while Learning from Human Reward

• [10]

  Computing Convex Coverage Sets for Faster Multi−Objective Coordination

  Diederik Roijers‚ Shimon Whiteson and Frans Oliehoek

  In Journal of Artificial Intelligence Research. Vol. 52. Pages 399−443. 2015.

  Details about Computing Convex Coverage Sets for Faster Multi−Objective Coordination | BibTeX data for Computing Convex Coverage Sets for Faster Multi−Objective Coordination | Download (pdf) of Computing Convex Coverage Sets for Faster Multi−Objective Coordination

• [11]

  Learning to Rank for Information Retrieval from User Interactions

  Katja Hofmann‚ Shimon Whiteson‚ Anne Schuth and Maarten de Rijke

  In SIGWEB Newsletter. No. Spring. Pages 1−7. April, 2014.

  Details about Learning to Rank for Information Retrieval from User Interactions | BibTeX data for Learning to Rank for Information Retrieval from User Interactions | Download (pdf) of Learning to Rank for Information Retrieval from User Interactions

• [12]

  Learning Potential Functions and their Representations for Multi−Task Reinforcement Learning

  Matthijs Snel and Shimon Whiteson

  In Autonomous Agents and Multi−Agent Systems. Vol. 28. No. 4. Pages 637−681. 2014.

  Details about Learning Potential Functions and their Representations for Multi−Task Reinforcement Learning | BibTeX data for Learning Potential Functions and their Representations for Multi−Task Reinforcement Learning | Download (pdf) of Learning Potential Functions and their Representations for Multi−Task Reinforcement Learning

• [13]

  Efficient Abstraction Selection in Reinforcement Learning

  Harm van Seijen‚ Shimon Whiteson and Leon Kester

  In Computational Intelligence. Vol. 30. No. 4. Pages 657−699. 2014.

  Details about Efficient Abstraction Selection in Reinforcement Learning | BibTeX data for Efficient Abstraction Selection in Reinforcement Learning | Download (pdf) of Efficient Abstraction Selection in Reinforcement Learning

• [14]

  Balancing Exploration and Exploitation in Listwise and Pairwise Online Learning to Rank for Information Retrieval

  Katja Hofmann‚ Shimon Whiteson and Maarten de Rijke

  In Information Retrieval. Vol. 16. No. 1. Pages 63−90. 2013.

  Details about Balancing Exploration and Exploitation in Listwise and Pairwise Online Learning to Rank for Information Retrieval | BibTeX data for Balancing Exploration and Exploitation in Listwise and Pairwise Online Learning to Rank for Information Retrieval | Download (pdf) of Balancing Exploration and Exploitation in Listwise and Pairwise Online Learning to Rank for Information Retrieval

• [15]

  Incremental Clustering and Expansion for Faster Optimal Planning in Decentralized POMDPs

  Frans Oliehoek‚ Matthijs Spaan‚ Christopher Amato and Shimon Whiteson

  In Journal of Artificial Intelligence Research. Vol. 46. Pages 449−509. 2013.

  Details about Incremental Clustering and Expansion for Faster Optimal Planning in Decentralized POMDPs | BibTeX data for Incremental Clustering and Expansion for Faster Optimal Planning in Decentralized POMDPs | Download (pdf) of Incremental Clustering and Expansion for Faster Optimal Planning in Decentralized POMDPs

• [16]

  Fidelity‚ Soundness‚ and Efficiency of Interleaved Comparison Methods

  Katja Hofmann‚ Shimon Whiteson and Maarten de Rijke

  In Transactions on Information Systems. Vol. 31(4). Pages 17:1−43. 2013.

  Details about Fidelity‚ Soundness‚ and Efficiency of Interleaved Comparison Methods | BibTeX data for Fidelity‚ Soundness‚ and Efficiency of Interleaved Comparison Methods | Download (pdf) of Fidelity‚ Soundness‚ and Efficiency of Interleaved Comparison Methods

• [17]

  A Survey of Multi−Objective Sequential Decision−Making

  Diederik Roijers‚ Peter Vamplew‚ Shimon Whiteson and Richard Dazeley

  In Journal of Artificial Intelligence Research. Vol. 48. Pages 67−113. 2013.

  Details about A Survey of Multi−Objective Sequential Decision−Making | BibTeX data for A Survey of Multi−Objective Sequential Decision−Making | Download (pdf) of A Survey of Multi−Objective Sequential Decision−Making

• [18]

  Exploiting Best−Match Equations for Efficient Reinforcement Learning

  Harm van Seijen‚ Shimon Whiteson‚ Hado van Hasselt and Marco Wiering

  In Journal of Machine Learning Research. Vol. 12. Pages 2045−2094. 2011.

  Details about Exploiting Best−Match Equations for Efficient Reinforcement Learning | BibTeX data for Exploiting Best−Match Equations for Efficient Reinforcement Learning | Download (pdf) of Exploiting Best−Match Equations for Efficient Reinforcement Learning

• [19]

  Introduction to the Special Issue on Empirical Evaluations in Reinforcement Learning

  Shimon Whiteson and Michael L. Littman

  In Machine Learning. Vol. 84. No. 1. Pages 1−6. 2011.

  Details about Introduction to the Special Issue on Empirical Evaluations in Reinforcement Learning | BibTeX data for Introduction to the Special Issue on Empirical Evaluations in Reinforcement Learning | Download (pdf) of Introduction to the Special Issue on Empirical Evaluations in Reinforcement Learning

• [20]

  Neuroevolutionary Reinforcement Learning for Generalized Control of Simulated Helicopters

  Rogier Koppejan and Shimon Whiteson

  In Evolutionary Intelligence. Vol. 4. Pages 219−241. 2011.

  Details about Neuroevolutionary Reinforcement Learning for Generalized Control of Simulated Helicopters | BibTeX data for Neuroevolutionary Reinforcement Learning for Generalized Control of Simulated Helicopters | Download (pdf) of Neuroevolutionary Reinforcement Learning for Generalized Control of Simulated Helicopters

• [21]

  The Reinforcement Learning Competitions

  Shimon Whiteson‚ Brian Tanner and Adam White

  In AI Magazine. Vol. 31. No. 2. Pages 81−94. 2010.

  Details about The Reinforcement Learning Competitions | BibTeX data for The Reinforcement Learning Competitions | Download (pdf) of The Reinforcement Learning Competitions

• [22]

  Critical Factors in the Empirical Performance of Temporal Difference and Evolutionary Methods for Reinforcement Learning

  Shimon Whiteson‚ Matthew E. Taylor and Peter Stone

  In Autonomous Agents and Multi−Agent Systems. Vol. 21. No. 1. Pages 1−27. 2010.

  Details about Critical Factors in the Empirical Performance of Temporal Difference and Evolutionary Methods for Reinforcement Learning | BibTeX data for Critical Factors in the Empirical Performance of Temporal Difference and Evolutionary Methods for Reinforcement Learning | Download (pdf) of Critical Factors in the Empirical Performance of Temporal Difference and Evolutionary Methods for Reinforcement Learning

• [23]

  Measurement of the Top Quark Mass with Dilepton Events Selected using Neuroevolution at CDF

  Aaltonen et al. (including Shimon Whiteson)

  In Physical Review Letters. Vol. 102. No. 15. Pages 2001. 2009.

  Details about Measurement of the Top Quark Mass with Dilepton Events Selected using Neuroevolution at CDF | BibTeX data for Measurement of the Top Quark Mass with Dilepton Events Selected using Neuroevolution at CDF | Download (pdf) of Measurement of the Top Quark Mass with Dilepton Events Selected using Neuroevolution at CDF

• [24]

  Machine Learning for Event Selection in High Energy Physics

  Shimon Whiteson and Daniel Whiteson

  In Engineering Applications of Artificial Intelligence. Vol. 22. Pages 1203−1217. 2009.

  Details about Machine Learning for Event Selection in High Energy Physics | BibTeX data for Machine Learning for Event Selection in High Energy Physics | Download (pdf) of Machine Learning for Event Selection in High Energy Physics

• [25]

  Empirical Studies in Action Selection with Reinforcement Learning

  Shimon Whiteson‚ Matthew E. Taylor and Peter Stone

  In Adaptive Behavior. Vol. 15(1). Pages 33−50. 2007.

  Details about Empirical Studies in Action Selection with Reinforcement Learning | BibTeX data for Empirical Studies in Action Selection with Reinforcement Learning | Download (pdf) of Empirical Studies in Action Selection with Reinforcement Learning

• [26]

  Evolutionary Function Approximation for Reinforcement Learning

  Shimon Whiteson and Peter Stone

  In Journal of Machine Learning Research. Vol. 7. Pages 877−917. 2006.

  Details about Evolutionary Function Approximation for Reinforcement Learning | BibTeX data for Evolutionary Function Approximation for Reinforcement Learning | Download (pdf) of Evolutionary Function Approximation for Reinforcement Learning

• [27]

  Evolving Keepaway Soccer Players through Task Decomposition

  Shimon Whiteson‚ Nate Kohl‚ Risto Miikkulainen and Peter Stone

  In Machine Learning. Vol. 59(1). Pages 5−30. 2005.

  Details about Evolving Keepaway Soccer Players through Task Decomposition | BibTeX data for Evolving Keepaway Soccer Players through Task Decomposition | Download (pdf) of Evolving Keepaway Soccer Players through Task Decomposition

• [28]

  Adaptive Job Routing and Scheduling

  Shimon Whiteson and Peter Stone

  In Engineering Applications of Artificial Intelligence. Vol. 17(7). Pages 855−869. 2004.

  Details about Adaptive Job Routing and Scheduling | BibTeX data for Adaptive Job Routing and Scheduling | Download (pdf) of Adaptive Job Routing and Scheduling

Conference papers

• [1]

  Deterministic and Discriminative Imitation (D2−Imitation): Revisiting Adversarial Imitation for Sample Efficiency

  Mingfei Sun‚ Sam Devlin‚ Katja Hofmann and Shimon Whiteson

  In AAAI 2022: Proceedings of the Thirty−Sixth AAAI Conference on Artificial Intelligence. February, 2022.

  Details about Deterministic and Discriminative Imitation (D2−Imitation): Revisiting Adversarial Imitation for Sample Efficiency | BibTeX data for Deterministic and Discriminative Imitation (D2−Imitation): Revisiting Adversarial Imitation for Sample Efficiency | Link to Deterministic and Discriminative Imitation (D2−Imitation): Revisiting Adversarial Imitation for Sample Efficiency

• [2]

  Bayesian Bellman Operators

  Matthew Fellows‚ Kristian Hartikainen and Shimon Whiteson

  In NeurIPS 2021: Proceedings of the Thirty−fifth Annual Conference on Neural Information Processing Systems. December, 2021.

  Details about Bayesian Bellman Operators | BibTeX data for Bayesian Bellman Operators | Link to Bayesian Bellman Operators

• [3]

  FACMAC: Factored Multi−Agent Centralised Policy Gradients

  Bei Peng‚ Tabish Rashid‚ Christian Schroeder de Witt‚ Pierre−Alexandre Kamienny‚ Philip Torr‚ Wendelin Boehmer and Shimon Whiteson

  In NeurIPS 2021: Proceedings of the Thirty−fifth Annual Conference on Neural Information Processing Systems. December, 2021.

  Details about FACMAC: Factored Multi−Agent Centralised Policy Gradients | BibTeX data for FACMAC: Factored Multi−Agent Centralised Policy Gradients | Link to FACMAC: Factored Multi−Agent Centralised Policy Gradients

• [4]

  Regularized Softmax Deep Multi−Agent Q−Learning

  Ling Pan‚ Tabish Rashid‚ Bei Peng‚ Longbo Huang and Shimon Whiteson

  In NeurIPS 2021: Proceedings of the Thirty−fifth Annual Conference on Neural Information Processing Systems. December, 2021.

  Details about Regularized Softmax Deep Multi−Agent Q−Learning | BibTeX data for Regularized Softmax Deep Multi−Agent Q−Learning | Link to Regularized Softmax Deep Multi−Agent Q−Learning

• [5]

  Snowflake: Scaling GNNs to high−dimensional continuous control via parameter freezing

  Charlie Blake‚ Vitaly Kurin‚ Maximilian Igl and Shimon Whiteson

  In NeurIPS 2021: Proceedings of the Thirty−fifth Annual Conference on Neural Information Processing Systems. December, 2021.

  Details about Snowflake: Scaling GNNs to high−dimensional continuous control via parameter freezing | BibTeX data for Snowflake: Scaling GNNs to high−dimensional continuous control via parameter freezing | Link to Snowflake: Scaling GNNs to high−dimensional continuous control via parameter freezing

• [6]

  Exploration in Approximate Hyper−State Space for Meta Reinforcement Learning

  Luisa M Zintgraf‚ Leo Feng‚ Cong Lu‚ Maximilian Igl‚ Kristian Hartikainen‚ Katja Hofmann and Shimon Whiteson

  In Marina Meila and Tong Zhang, editors, Proceedings of the 38th International Conference on Machine Learning. Vol. 139 of Proceedings of Machine Learning Research. Pages 12991–13001. PMLR. 2021.

  Details about Exploration in Approximate Hyper−State Space for Meta Reinforcement Learning | BibTeX data for Exploration in Approximate Hyper−State Space for Meta Reinforcement Learning | Link to Exploration in Approximate Hyper−State Space for Meta Reinforcement Learning

• [7]

  Breaking the Deadly Triad with a Target Network

  Shangtong Zhang‚ Hengshuai Yao and Shimon Whiteson

  In Marina Meila and Tong Zhang, editors, Proceedings of the 38th International Conference on Machine Learning. Vol. 139 of Proceedings of Machine Learning Research. Pages 12621–12631. PMLR. 2021.

  Details about Breaking the Deadly Triad with a Target Network | BibTeX data for Breaking the Deadly Triad with a Target Network | Link to Breaking the Deadly Triad with a Target Network

• [8]

  Average−Reward Off−Policy Policy Evaluation with Function Approximation

  Shangtong Zhang‚ Yi Wan‚ Richard S Sutton and Shimon Whiteson

  In Marina Meila and Tong Zhang, editors, Proceedings of the 38th International Conference on Machine Learning. Vol. 139 of Proceedings of Machine Learning Research. Pages 12578–12588. PMLR. 2021.

  Details about Average−Reward Off−Policy Policy Evaluation with Function Approximation | BibTeX data for Average−Reward Off−Policy Policy Evaluation with Function Approximation | Link to Average−Reward Off−Policy Policy Evaluation with Function Approximation

• [9]

  Tesseract: Tensorised Actors for Multi−Agent Reinforcement Learning

  Anuj Mahajan‚ Mikayel Samvelyan‚ Lei Mao‚ Viktor Makoviychuk‚ Animesh Garg‚ Jean Kossaifi‚ Shimon Whiteson‚ Yuke Zhu and Animashree Anandkumar

  In Marina Meila and Tong Zhang, editors, Proceedings of the 38th International Conference on Machine Learning. Vol. 139 of Proceedings of Machine Learning Research. Pages 7301–7312. PMLR. 2021.

  Details about Tesseract: Tensorised Actors for Multi−Agent Reinforcement Learning | BibTeX data for Tesseract: Tensorised Actors for Multi−Agent Reinforcement Learning | Link to Tesseract: Tensorised Actors for Multi−Agent Reinforcement Learning

• [10]

  Randomized Entity−wise Factorization for Multi−Agent Reinforcement Learning

  Shariq Iqbal‚ Christian A Schroeder De Witt‚ Bei Peng‚ Wendelin Boehmer‚ Shimon Whiteson and Fei Sha

  In Marina Meila and Tong Zhang, editors, Proceedings of the 38th International Conference on Machine Learning. Vol. 139 of Proceedings of Machine Learning Research. Pages 4596–4606. PMLR. 2021.

  Details about Randomized Entity−wise Factorization for Multi−Agent Reinforcement Learning | BibTeX data for Randomized Entity−wise Factorization for Multi−Agent Reinforcement Learning | Link to Randomized Entity−wise Factorization for Multi−Agent Reinforcement Learning

• [11]

  UneVEn: Universal Value Exploration for Multi−Agent Reinforcement Learning

  Tarun Gupta‚ Anuj Mahajan‚ Bei Peng‚ Wendelin Boehmer and Shimon Whiteson

  In Marina Meila and Tong Zhang, editors, Proceedings of the 38th International Conference on Machine Learning. Vol. 139 of Proceedings of Machine Learning Research. Pages 3930–3941. PMLR. 2021.

  Details about UneVEn: Universal Value Exploration for Multi−Agent Reinforcement Learning | BibTeX data for UneVEn: Universal Value Exploration for Multi−Agent Reinforcement Learning | Link to UneVEn: Universal Value Exploration for Multi−Agent Reinforcement Learning

• [12]

  Mean−Variance Policy Iteration for Risk−Averse Reinforcement Learning

  Shangtong Zhang‚ Bo Liu and Shimon Whiteson

  In AAAI 2021: Proceedings of the Thirty−Fifth AAAI Conference on Artificial Intelligence. February, 2021.

  Details about Mean−Variance Policy Iteration for Risk−Averse Reinforcement Learning | BibTeX data for Mean−Variance Policy Iteration for Risk−Averse Reinforcement Learning | Link to Mean−Variance Policy Iteration for Risk−Averse Reinforcement Learning

• [13]

  Transient Non−stationarity and Generalisation in Deep Reinforcement Learning

  Maximilian Igl‚ Gregory Farquhar‚ Jelena Luketina‚ Wendelin Boehmer and Shimon Whiteson

  In ICLR 2021: Proceedings of the ninth International Conference on Learning Representations. May, 2021.

  Details about Transient Non−stationarity and Generalisation in Deep Reinforcement Learning | BibTeX data for Transient Non−stationarity and Generalisation in Deep Reinforcement Learning | Link to Transient Non−stationarity and Generalisation in Deep Reinforcement Learning

• [14]

  My Body is a Cage: the Role of Morphology in Graph−Based Incompatible Control

  Vitaly Kurin‚ Maximilian Igl‚ Tim Rocktachel‚ Wendelin Boehmer and Shimon Whiteson

  In ICLR 2021: Proceedings of the ninth International Conference on Learning Representations. May, 2021.

  Details about My Body is a Cage: the Role of Morphology in Graph−Based Incompatible Control | BibTeX data for My Body is a Cage: the Role of Morphology in Graph−Based Incompatible Control | Link to My Body is a Cage: the Role of Morphology in Graph−Based Incompatible Control

• [15]

  RODE: Learning Roles to Decompose Multi−Agent Tasks

  Tonghan Wang‚ Tarun Gupta‚ Anuj Mahajan‚ Bei Peng‚ Shimon Whiteson and Chongjie Zhang

  In ICLR 2021: Proceedings of the ninth International Conference on Learning Representations. May, 2021.

  Details about RODE: Learning Roles to Decompose Multi−Agent Tasks | BibTeX data for RODE: Learning Roles to Decompose Multi−Agent Tasks | Link to RODE: Learning Roles to Decompose Multi−Agent Tasks

• [16]

  Learning Retrospective Knowledge with Reverse Reinforcement Learning

  Shangtong Zhang‚ Vivek Veeriah and Shimon Whiteson

  In NeurIPS 2020: Proceedings of the Thirty−fourth Annual Conference on Neural Information Processing Systems. December, 2020.

  Details about Learning Retrospective Knowledge with Reverse Reinforcement Learning | BibTeX data for Learning Retrospective Knowledge with Reverse Reinforcement Learning | Download (pdf) of Learning Retrospective Knowledge with Reverse Reinforcement Learning

• [17]

  Weighted QMIX: Expanding Monotonic Value Function Factorisation for Deep Multi−Agent Reinforcement Learning

  Tabish Rashid‚ Gregory Farquhar‚ Bei Peng and Shimon Whiteson

  In NeurIPS 2020: Proceedings of the Thirty−fourth Annual Conference on Neural Information Processing Systems. December, 2020.

  Details about Weighted QMIX: Expanding Monotonic Value Function Factorisation for Deep Multi−Agent Reinforcement Learning | BibTeX data for Weighted QMIX: Expanding Monotonic Value Function Factorisation for Deep Multi−Agent Reinforcement Learning | Download (pdf) of Weighted QMIX: Expanding Monotonic Value Function Factorisation for Deep Multi−Agent Reinforcement Learning

• [18]

  Can Q−Learning with Graph Networks Learn a Generalizable Branching Heuristic for a SAT Solver?

  Vitaly Kurin‚ Saad Godil‚ Shimon Whiteson and Bryan Catanzaro

  In NeurIPS 2020: Proceedings of the Thirty−fourth Annual Conference on Neural Information Processing Systems. December, 2020.

  Details about Can Q−Learning with Graph Networks Learn a Generalizable Branching Heuristic for a SAT Solver? | BibTeX data for Can Q−Learning with Graph Networks Learn a Generalizable Branching Heuristic for a SAT Solver? | Download (pdf) of Can Q−Learning with Graph Networks Learn a Generalizable Branching Heuristic for a SAT Solver?

• [19]

  Provably Convergent Two−Timescale Off−Policy Actor−Critic with Function Approximation

  Shangtong Zhang‚ Bo Liu‚ Hengshuai Yao and Shimon Whiteson

  In ICML 2020: Proceedings of the Thirty−Seventh International Conference on Machine Learning. July, 2020.

  Details about Provably Convergent Two−Timescale Off−Policy Actor−Critic with Function Approximation | BibTeX data for Provably Convergent Two−Timescale Off−Policy Actor−Critic with Function Approximation | Download (pdf) of Provably Convergent Two−Timescale Off−Policy Actor−Critic with Function Approximation

• [20]

  GradientDICE: Rethinking Generalized Offline Estimation of Stationary Values

  Shangtong Zhang‚ Bo Liu and Shimon Whiteson

  In ICML 2020: Proceedings of the Thirty−Seventh International Conference on Machine Learning. July, 2020.

  Details about GradientDICE: Rethinking Generalized Offline Estimation of Stationary Values | BibTeX data for GradientDICE: Rethinking Generalized Offline Estimation of Stationary Values | Download (pdf) of GradientDICE: Rethinking Generalized Offline Estimation of Stationary Values

• [21]

  Deep Coordination Graphs

  Wendelin Boehmer‚ Vitaly Kurin and Shimon Whiteson

  In ICML 2020: Proceedings of the Thirty−Seventh International Conference on Machine Learning. July, 2020.

  Details about Deep Coordination Graphs | BibTeX data for Deep Coordination Graphs | Download (pdf) of Deep Coordination Graphs

• [22]

  Growing Action Spaces

  Gregory Farquhar‚ Laura Gustafson‚ Zeming Lin‚ Shimon Whiteson‚ Nicolas Usunier and Gabriel Synnaeve

  In ICML 2020: Proceedings of the Thirty−Seventh International Conference on Machine Learning. July, 2020.

  Details about Growing Action Spaces | BibTeX data for Growing Action Spaces | Download (pdf) of Growing Action Spaces

• [23]

  Deep Residual Reinforcement Learning

  Shangtong Zhang‚ Wendelin Boehmer and Shimon Whiteson

  In AAMAS 2020: Proceedings of the Nineteenth International Joint Conference on Autonomous Agents and Multi−Agent Systems. May, 2020.

  Awarded Best Paper.

  Details about Deep Residual Reinforcement Learning | BibTeX data for Deep Residual Reinforcement Learning | Download (pdf) of Deep Residual Reinforcement Learning

• [24]

  Maximizing Information Gain in Partially Observable Environments via Prediction Rewards

  Yash Satsangi‚ Sungsu Lim‚ Shimon Whiteson‚ Frans Oliehoek and Martha White

  In AAMAS 2020: Proceedings of the Nineteenth International Joint Conference on Autonomous Agents and Multi−Agent Systems. May, 2020.

  Details about Maximizing Information Gain in Partially Observable Environments via Prediction Rewards | BibTeX data for Maximizing Information Gain in Partially Observable Environments via Prediction Rewards | Download (pdf) of Maximizing Information Gain in Partially Observable Environments via Prediction Rewards

• [25]

  Optimistic Exploration Even With A Pessimistic Initialisation

  Tabish Rashid‚ Bei Peng‚ Wendelin Boehmer and Shimon Whiteson

  In ICLR 2020: Proceedings of the Eighth International Conference on Learning Representations. May, 2020.

  Details about Optimistic Exploration Even With A Pessimistic Initialisation | BibTeX data for Optimistic Exploration Even With A Pessimistic Initialisation | Download (pdf) of Optimistic Exploration Even With A Pessimistic Initialisation

• [26]

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

  Luisa Zintgraf‚ Kyriacos Shiarlis‚ Maximilian Igl‚ Sebastian Schulze‚ Yarin Gal‚ Katja Hofmann and Shimon Whiteson

  In ICLR 2020: Proceedings of the Eighth International Conference on Learning Representations. May, 2020.

  Details about VariBAD: A Very Good Method for Bayes−Adaptive Deep RL via Meta−Learning | BibTeX data for VariBAD: A Very Good Method for Bayes−Adaptive Deep RL via Meta−Learning | Download (pdf) of VariBAD: A Very Good Method for Bayes−Adaptive Deep RL via Meta−Learning

• [27]

  Multi−Agent Common Knowledge Reinforcement Learning

  Christian Schroeder de Witt‚ Jakob Foerster‚ Gregory Farquhar‚ Philip H. S. Torr‚ Wendelin Boehmer and Shimon Whiteson

  In NeurIPS 2019: Proceedings of the Thirty−third Annual Conference on Neural Information Processing Systems. December, 2019.

  Details about Multi−Agent Common Knowledge Reinforcement Learning | BibTeX data for Multi−Agent Common Knowledge Reinforcement Learning | Download (pdf) of Multi−Agent Common Knowledge Reinforcement Learning

• [28]

  MAVEN: Multi−Agent Variational Exploration

  Anuj Mahajan‚ Tabish Rashid‚ Mikayel Samvelyan and Shimon Whiteson

  In NeurIPS 2019: Proceedings of the Thirty−third Annual Conference on Neural Information Processing Systems. December, 2019.

  Details about MAVEN: Multi−Agent Variational Exploration | BibTeX data for MAVEN: Multi−Agent Variational Exploration | Download (pdf) of MAVEN: Multi−Agent Variational Exploration

• [29]

  Loaded DiCE: Trading off Bias and Variance in Any−Order Score Function Estimators for Reinforcement Learning

  Gregory Farquhar‚ Shimon Whiteson and Jakob Foerster

  In NeurIPS 2019: Proceedings of the Thirty−third Annual Conference on Neural Information Processing Systems. December, 2019.

  Details about Loaded DiCE: Trading off Bias and Variance in Any−Order Score Function Estimators for Reinforcement Learning | BibTeX data for Loaded DiCE: Trading off Bias and Variance in Any−Order Score Function Estimators for Reinforcement Learning | Download (pdf) of Loaded DiCE: Trading off Bias and Variance in Any−Order Score Function Estimators for Reinforcement Learning

• [30]

  VIREL: A Variational Inference Framework for Reinforcement Learning

  Matthew Fellows‚ Anuj Mahajan‚ Tim Rudner and Shimon Whiteson

  In NeurIPS 2019: Proceedings of the Thirty−third Annual Conference on Neural Information Processing Systems. December, 2019.

  Details about VIREL: A Variational Inference Framework for Reinforcement Learning | BibTeX data for VIREL: A Variational Inference Framework for Reinforcement Learning | Download (pdf) of VIREL: A Variational Inference Framework for Reinforcement Learning

• [31]

  DAC: The Double Actor−Critic Architecture for Learning Options

  Shangtong Zhang and Shimon Whiteson

  In NeurIPS 2019: Proceedings of the Thirty−third Annual Conference on Neural Information Processing Systems. December, 2019.

  Details about DAC: The Double Actor−Critic Architecture for Learning Options | BibTeX data for DAC: The Double Actor−Critic Architecture for Learning Options | Download (pdf) of DAC: The Double Actor−Critic Architecture for Learning Options

• [32]

  Generalized Off−Policy Actor−Critic

  Shangtong Zhang‚ Wendelin Boehmer and Shimon Whiteson

  In NeurIPS 2019: Proceedings of the Thirty−third Annual Conference on Neural Information Processing Systems. December, 2019.

  Details about Generalized Off−Policy Actor−Critic | BibTeX data for Generalized Off−Policy Actor−Critic | Download (pdf) of Generalized Off−Policy Actor−Critic

• [33]

  Fast Efficient Hyperparameter Tuning for Policy Gradient Methods

  Supratik Paul‚ Vitaly Kurin and Shimon Whiteson

  In NeurIPS 2019: Proceedings of the Thirty−third Annual Conference on Neural Information Processing Systems. December, 2019.

  Details about Fast Efficient Hyperparameter Tuning for Policy Gradient Methods | BibTeX data for Fast Efficient Hyperparameter Tuning for Policy Gradient Methods | Download (pdf) of Fast Efficient Hyperparameter Tuning for Policy Gradient Methods

• [34]

  A Survey of Reinforcement Learning Informed by Natural Language

  Jelena Luketina‚ Nantas Nardelli‚ Gregory Farquhar‚ Jakob Foerster‚ Jacob Andreas‚ Edward Grefenstette‚ Shimon Whiteson and Tim Rocktaschel

  In IJCAI 2019: Proceedings of the Twenty−Eighth International Joint Conference on Artificial Intelligence. August, 2019.

  Details about A Survey of Reinforcement Learning Informed by Natural Language | BibTeX data for A Survey of Reinforcement Learning Informed by Natural Language | Download (pdf) of A Survey of Reinforcement Learning Informed by Natural Language

• [35]

  A Baseline for Any Order Gradient Estimation in Stochastic Computation Graphs

  Jingkai Mao‚ Jakob Foerster‚ Tim Rocktäschel‚ Maruan Al−Shedivat‚ Gregory Farquhar and Shimon Whiteson

  In ICML 2019: Proceedings of the Thirty−Sixth International Conference on Machine Learning. June, 2019.

  Details about A Baseline for Any Order Gradient Estimation in Stochastic Computation Graphs | BibTeX data for A Baseline for Any Order Gradient Estimation in Stochastic Computation Graphs | Download (pdf) of A Baseline for Any Order Gradient Estimation in Stochastic Computation Graphs

• [36]

  Fingerprint Policy Optimisation for Robust Reinforcement Learning

  Supratik Paul‚ Michael A. Osborne and Shimon Whiteson

  In ICML 2019: Proceedings of the Thirty−Sixth International Conference on Machine Learning. June, 2019.

  Details about Fingerprint Policy Optimisation for Robust Reinforcement Learning | BibTeX data for Fingerprint Policy Optimisation for Robust Reinforcement Learning | Download (pdf) of Fingerprint Policy Optimisation for Robust Reinforcement Learning

• [37]

  Fast Context Adaptation via Meta−Learning

  Luisa Zintgraf‚ Kyriacos Shiarlis‚ Vitaly Kurin‚ Katja Hofmann and Shimon Whiteson

  In ICML 2019: Proceedings of the Thirty−Sixth International Conference on Machine Learning. June, 2019.

  Details about Fast Context Adaptation via Meta−Learning | BibTeX data for Fast Context Adaptation via Meta−Learning | Download (pdf) of Fast Context Adaptation via Meta−Learning

• [38]

  Bayesian Action Decoder for Deep Multi−Agent Reinforcement Learning

  Jakob N. Foerster‚ H. Francis Song‚ Edward Hughes‚ Neil Burch‚ Iain Dunning‚ Shimon Whiteson‚ Matthew M. Botvinick and Michael Bowling

  In ICML 2019: Proceedings of the Thirty−Sixth International Conference on Machine Learning. June, 2019.

  Details about Bayesian Action Decoder for Deep Multi−Agent Reinforcement Learning | BibTeX data for Bayesian Action Decoder for Deep Multi−Agent Reinforcement Learning | Download (pdf) of Bayesian Action Decoder for Deep Multi−Agent Reinforcement Learning

• [39]

  A Baseline for Any Order Gradient Estimation in Stochastic Computation Graphs

  Jingkai Mao‚ Jakob Foerster‚ Tim Rocktäschel‚ Maruan Al−Shedivat‚ Gregory Farquhar and Shimon Whiteson

  In ICML 2019: Proceedings of the Thirty−Sixth International Conference on Machine Learning. June, 2019.

  Details about A Baseline for Any Order Gradient Estimation in Stochastic Computation Graphs | BibTeX data for A Baseline for Any Order Gradient Estimation in Stochastic Computation Graphs | Download (pdf) of A Baseline for Any Order Gradient Estimation in Stochastic Computation Graphs

• [40]

  Learning from Demonstration in the Wild

  Feryal Behbahani‚ Kyriacos Shiarlis‚ Xi Chen‚ Vitaly Kurin‚ Sudhanshu Kasewa‚ Ciprian Stirbu‚ Joao Gomes‚ Supratik Paul‚ Frans Oliehoek‚ Joao Messias and Shimon Whiteson

  In ICRA 2019: Proceedings of the 2019 IEEE International Conference on Robotics and Automation. May, 2019.

  Details about Learning from Demonstration in the Wild | BibTeX data for Learning from Demonstration in the Wild | Download (pdf) of Learning from Demonstration in the Wild

• [41]

  Stable Opponent Shaping in Differentiable Games

  Alistair Letcher‚ Jakob Foerster‚ David Balduzzi‚ Tim Rocktaschel and Shimon Whiteson

  In ICLR 2019: Proceedings of the Seventh International Conference on Learning Representations. May, 2019.

  Details about Stable Opponent Shaping in Differentiable Games | BibTeX data for Stable Opponent Shaping in Differentiable Games | Download (pdf) of Stable Opponent Shaping in Differentiable Games

• [42]

  TACO: Learning Task Decomposition via Temporal Alignment for Control

  Kyriacos Shiarlis‚ Markus Wulfmeier‚ Sasha Salter‚ Shimon Whiteson and Ingmar Posner

  In ICML 2018: Proceedings of the Thirty−Fifth International Conference on Machine Learning. July, 2018.

  Details about TACO: Learning Task Decomposition via Temporal Alignment for Control | BibTeX data for TACO: Learning Task Decomposition via Temporal Alignment for Control | Download (pdf) of TACO: Learning Task Decomposition via Temporal Alignment for Control

• [43]

  QMIX: Monotonic Value Function Factorisation for Deep Multi−Agent Reinforcement Learning

  Tabish Rashid‚ Mikayel Samvelyan‚ Christian Schroeder de Witt‚ Gregory Farquhar‚ Jakob Foerster and Shimon Whiteson

  In ICML 2018: Proceedings of the Thirty−Fifth International Conference on Machine Learning. July, 2018.

  Details about QMIX: Monotonic Value Function Factorisation for Deep Multi−Agent Reinforcement Learning | BibTeX data for QMIX: Monotonic Value Function Factorisation for Deep Multi−Agent Reinforcement Learning | Download (pdf) of QMIX: Monotonic Value Function Factorisation for Deep Multi−Agent Reinforcement Learning

• [44]

  Deep Variational Reinforcement Learning for POMDPs

  Maximillian Igl‚ Luisa Zintgraf‚ Tuan Anh Le‚ Frank Wood and Shimon Whiteson

  In ICML 2018: Proceedings of the Thirty−Fifth International Conference on Machine Learning. July, 2018.

  Details about Deep Variational Reinforcement Learning for POMDPs | BibTeX data for Deep Variational Reinforcement Learning for POMDPs | Download (pdf) of Deep Variational Reinforcement Learning for POMDPs

• [45]

  Fourier Policy Gradients

  Matthew Fellows‚ Kamil Ciosek and Shimon Whiteson

  In ICML 2018: Proceedings of the Thirty−Fifth International Conference on Machine Learning. July, 2018.

  Details about Fourier Policy Gradients | BibTeX data for Fourier Policy Gradients | Download (pdf) of Fourier Policy Gradients

• [46]

  DiCE: The Infinitely Differentiable Monte Carlo Estimator

  Jakob Foerster‚ Gregory Farquhar‚ Maruan Al−Shedivat‚ Tim Rocktaschel‚ Eric P. Xing and Shimon Whiteson

  In ICML 2018: Proceedings of the Thirty−Fifth International Conference on Machine Learning. July, 2018.

  Details about DiCE: The Infinitely Differentiable Monte Carlo Estimator | BibTeX data for DiCE: The Infinitely Differentiable Monte Carlo Estimator | Download (pdf) of DiCE: The Infinitely Differentiable Monte Carlo Estimator

• [47]

  TreeQN and ATreeC: Differentiable Tree−Structured Models for Deep Reinforcement Learning

  Gregory Farquhar‚ Tim Rocktaschel‚ Maximilian Igl and Shimon Whiteson

  In ICLR 2018: Proceedings of the Sixth International Conference on Learning Representations. April, 2018.

  Details about TreeQN and ATreeC: Differentiable Tree−Structured Models for Deep Reinforcement Learning | BibTeX data for TreeQN and ATreeC: Differentiable Tree−Structured Models for Deep Reinforcement Learning | Link to TreeQN and ATreeC: Differentiable Tree−Structured Models for Deep Reinforcement Learning

• [48]

  Learning with Opponent−Learning Awareness

  Jakob Foerster‚ Richard Chen‚ Maruan Al−Shedivat‚ Shimon Whiteson‚ Pieter Abbeel and Igor Mordatch

  In AAMAS 2018: Proceedings of the Seventeenth International Joint Conference on Autonomous Agents and Multi−Agent Systems. July, 2018.

  Details about Learning with Opponent−Learning Awareness | BibTeX data for Learning with Opponent−Learning Awareness | Download (pdf) of Learning with Opponent−Learning Awareness

• [49]

  Alternating Optimisation and Quadrature for Robust Control

  Supratik Paul‚ Konstantinos Chatzilygeroudis‚ Kamil Ciosek‚ Jean−Baptiste Mouret‚ Michael Osborne and Shimon Whiteson

  In AAAI 2018: Proceedings of the Thirty−Second AAAI Conference on Artificial Intelligence. February, 2018.

  Details about Alternating Optimisation and Quadrature for Robust Control | BibTeX data for Alternating Optimisation and Quadrature for Robust Control | Download (pdf) of Alternating Optimisation and Quadrature for Robust Control

• [50]

  Counterfactual Multi−Agent Policy Gradients

  Jakob Foerster‚ Gregory Farquhar‚ Triantafyllos Afouras‚ Nantas Nardelli and Shimon Whiteson

  In AAAI 2018: Proceedings of the Thirty−Second AAAI Conference on Artificial Intelligence. February, 2018.

  Awarded Outstanding Student Paper.

  Details about Counterfactual Multi−Agent Policy Gradients | BibTeX data for Counterfactual Multi−Agent Policy Gradients | Download (pdf) of Counterfactual Multi−Agent Policy Gradients

• [51]

  Expected Policy Gradients

  Kamil Ciosek and Shimon Whiteson

  In AAAI 2018: Proceedings of the Thirty−Second AAAI Conference on Artificial Intelligence. February, 2018.

  Details about Expected Policy Gradients | BibTeX data for Expected Policy Gradients | Download (pdf) of Expected Policy Gradients

• [52]

  Dynamic−Depth Context Tree Weighting

  João Messias and Shimon Whiteson

  In NeurIPS 2017: Proceedings of the Thirty−First Annual Conference on Neural Information Processing Systems. December, 2017.

  NVAIL Pioneering Research Award.

  Details about Dynamic−Depth Context Tree Weighting | BibTeX data for Dynamic−Depth Context Tree Weighting | Download (pdf) of Dynamic−Depth Context Tree Weighting

• [53]

  Real−Time Resource Allocation for Tracking Systems

  Yash Satsangi‚ Shimon Whiteson‚ Frans Oliehoek and Henri Bouma

  In UAI 2017: Proceedings of the Conference on Uncertainty in Artificial Intelligence. Pages 3220−3227. July, 2017.

  Details about Real−Time Resource Allocation for Tracking Systems | BibTeX data for Real−Time Resource Allocation for Tracking Systems | Download (pdf) of Real−Time Resource Allocation for Tracking Systems

• [54]

  Acquiring Social Interaction Behaviours for Telepresence Robots via Deep Learning from Demonstration

  Kyriacos Shiarlis‚ João Messias and Shimon Whiteson

  In IROS 2017: Proceedings of the 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems. September, 2017.

  Details about Acquiring Social Interaction Behaviours for Telepresence Robots via Deep Learning from Demonstration | BibTeX data for Acquiring Social Interaction Behaviours for Telepresence Robots via Deep Learning from Demonstration | Download (pdf) of Acquiring Social Interaction Behaviours for Telepresence Robots via Deep Learning from Demonstration

• [55]

  Stabilising Experience Replay for Deep Multi−Agent Reinforcement Learning

  Jakob Foerster‚ Nantas Nardelli‚ Greg Farquhar‚ Phil Torr‚ Pushmeet Kohli and Shimon Whiteson

  In ICML 2017: Proceedings of the Thirty−Fourth International Conference on Machine Learning. June, 2017.

  Details about Stabilising Experience Replay for Deep Multi−Agent Reinforcement Learning | BibTeX data for Stabilising Experience Replay for Deep Multi−Agent Reinforcement Learning | Download (pdf) of Stabilising Experience Replay for Deep Multi−Agent Reinforcement Learning

• [56]

  Rapidly Exploring Learning Trees

  Kyriacos Shiarlis‚ João Messias and Shimon Whiteson

  In ICRA 2017: Proceedings of the 2017 IEEE International Conference on Robotics and Automation. May, 2017.

  Details about Rapidly Exploring Learning Trees | BibTeX data for Rapidly Exploring Learning Trees | Download (pdf) of Rapidly Exploring Learning Trees

• [57]

  OFFER: Off−Environment Reinforcement Learning

  Kamil Ciosek and Shimon Whiteson

  In AAAI 2017: Proceedings of the Thirty−First AAAI Conference on Artificial Intelligence. Pages 1819−1825. February, 2017.

  Details about OFFER: Off−Environment Reinforcement Learning | BibTeX data for OFFER: Off−Environment Reinforcement Learning | Download (pdf) of OFFER: Off−Environment Reinforcement Learning

• [58]

  Real−Time Resource Allocation for Tracking Systems

  Yash Satsangi‚ Shimon Whiteson‚ Frans Oliehoek and Henri Bouma

  In UAI 2017: Proceedings of the Conference on Uncertainty in Artificial Intelligence. Pages 3220−3227. July, 2016.

  Details about Real−Time Resource Allocation for Tracking Systems | BibTeX data for Real−Time Resource Allocation for Tracking Systems | Link to Real−Time Resource Allocation for Tracking Systems

• [59]

  Learning to Communicate with Deep Multi−Agent Reinforcement Learning

  Jakob Foerster‚ Yannis Assael‚ Nando de Freitas and Shimon Whiteson

  In NeurIPS 2016: Proceedings of the Thirtieth Annual Conference on Neural Information Processing Systems. December, 2016.

  Details about Learning to Communicate with Deep Multi−Agent Reinforcement Learning | BibTeX data for Learning to Communicate with Deep Multi−Agent Reinforcement Learning | Download (pdf) of Learning to Communicate with Deep Multi−Agent Reinforcement Learning

• [60]

  PAC Greedy Maximization with Efficient Bounds on Information Gain for Sensor Selection

  Yash Satsangi‚ Shimon Whiteson and Frans Oliehoek

  In IJCAI 2016: Proceedings of the Twenty−Fifth International Joint Conference on Artificial Intelligence. Pages 3220−3227. July, 2016.

  Details about PAC Greedy Maximization with Efficient Bounds on Information Gain for Sensor Selection | BibTeX data for PAC Greedy Maximization with Efficient Bounds on Information Gain for Sensor Selection | Download (pdf) of PAC Greedy Maximization with Efficient Bounds on Information Gain for Sensor Selection

• [61]

  Inverse Reinforcement Learning from Failure

  Kyriacos Shiarlis‚ João Messias and Shimon Whiteson

  In AAMAS 2016: Proceedings of the Fifteenth International Joint Conference on Autonomous Agents and Multi−Agent Systems. Pages 1060−1068. May, 2016.

  Nominated for Best Student Paper.

  Details about Inverse Reinforcement Learning from Failure | BibTeX data for Inverse Reinforcement Learning from Failure | Download (pdf) of Inverse Reinforcement Learning from Failure

• [62]

  Multileave Gradient Descent for Fast Online Learning to Rank

  Anne Schuth‚ Harrie Oosterhuis‚ Shimon Whiteson and Maarten de Rijke

  In WSDM 2016: Proceedings of the Ninth ACM International Conference on Web Search and Data Mining. Pages 457−466. February, 2016.

  Details about Multileave Gradient Descent for Fast Online Learning to Rank | BibTeX data for Multileave Gradient Descent for Fast Online Learning to Rank | Download (pdf) of Multileave Gradient Descent for Fast Online Learning to Rank

• [63]

  Variational Multi−Objective Coordination

  Diederik Roijers‚ Shimon Whiteson‚ Alex Ihler and Frans Oliehoek

  In NeurIPS 2015: Proceedings of the Twenty−Ninth Annual Conference on Neural Information Processing Systems‚ Workshop on Learning‚ Inference and Control of Multi−Agent Systems. December, 2015.

  Details about Variational Multi−Objective Coordination | BibTeX data for Variational Multi−Objective Coordination | Download (pdf) of Variational Multi−Objective Coordination

• [64]

  Queued Pareto Local Search for Multi−objective Decision Making

  Maarten de Waard‚ Maarten Inja‚ Chiel Kooijman‚ Diederik Roijers and Shimon Whiteson

  In BNAIC 2015: Proceedings of the Twenty−Seventh Benelux Conference on Artificial Intelligence. November, 2015.

  Details about Queued Pareto Local Search for Multi−objective Decision Making | BibTeX data for Queued Pareto Local Search for Multi−objective Decision Making | Download (pdf) of Queued Pareto Local Search for Multi−objective Decision Making

• [65]

  Copeland Dueling Bandits

  Masrour Zoghi‚ Zohar Karnin‚ Shimon Whiteson and Maarten de Rijke

  In NeurIPS 2015: Proceedings of the Twenty−Ninth Annual Conference on Neural Information Processing Systems. December, 2015.

  Details about Copeland Dueling Bandits | BibTeX data for Copeland Dueling Bandits | Download (pdf) of Copeland Dueling Bandits

• [66]

  Pareto Local Policy Search for MOMDP Planning

  Chiel Kooijman‚ Maarten de Waard‚ Maarten Inja‚ Diederik Roijers and Shimon Whiteson

  In ESANN 2015: Proceedings of the 23rd European Symposium on Artificial Neural Networks‚ Special Session on Emerging Techniques and Applications in Multi−Objective Reinforcement Learning. Pages 53−58. April, 2015.

  Details about Pareto Local Policy Search for MOMDP Planning | BibTeX data for Pareto Local Policy Search for MOMDP Planning | Download (pdf) of Pareto Local Policy Search for MOMDP Planning

• [67]

  A Large−Scale Study of Agents Learning from Human Reward

  Guangliang Li‚ Hayley Hung and Shimon Whiteson

  In AAMAS 2015: Proceedings of the Fourteenth International Joint Conference on Autonomous Agents and Multi−Agent Systems. Pages 1771−1772. May, 2015.

  Extended Abstract.

  Details about A Large−Scale Study of Agents Learning from Human Reward | BibTeX data for A Large−Scale Study of Agents Learning from Human Reward | Download (pdf) of A Large−Scale Study of Agents Learning from Human Reward

• [68]

  TERESA: A Socially Intelligent Semi−autonomous Telepresence System

  Kyriacos Shiarlis‚ Joao Messias‚ Maarten van Someren‚ Shimon Whiteson‚ Jaebok Kim‚ Jered Vroon‚ Gwenn Englebienne‚ Khiet Truong‚ Vanessa Evers‚ Noe Perez−Higueras‚ Ignacio Perez−Hurtado‚ Rafael Ramon−Vigo‚ Fernando Caballero‚ Luis Merino‚ Jie Shen‚ Stavros Petridis‚ Maja Pantic‚ Lasse Hedman‚ Marten Scherlund‚ Raphael Koster and Herve Michel

  In ICRA 2015: Proceedings of the IEEE International Conference on Robotics and Automation‚ Workshop on Machine Learning for Social Robotics. May, 2015.

  Details about TERESA: A Socially Intelligent Semi−autonomous Telepresence System | BibTeX data for TERESA: A Socially Intelligent Semi−autonomous Telepresence System | Download (pdf) of TERESA: A Socially Intelligent Semi−autonomous Telepresence System

• [69]

  Inverse Reinforcement Learning from Failure

  Kyriacos Shiarlis‚ Joao Messias‚ Maarten van Someren and Shimon Whiteson

  In RSS 2015: Proceedings of the 2015 Robotics: Science and Systems Conference‚ Workshop on Learning from Demonstration: Inverse Optimal Control‚ Reinforcement Learning‚ and Lifelong Learning. July, 2015.

  Details about Inverse Reinforcement Learning from Failure | BibTeX data for Inverse Reinforcement Learning from Failure | Download (pdf) of Inverse Reinforcement Learning from Failure

• [70]

  Why Multi−Objective Reinforcement Learning?

  Diederik Roijers‚ Shimon Whiteson‚ Peter Vamplew and Richard Dazeley

  In EWRL 2015: Proceedings of the Twelfth European Workshop on Reinforcement Learning. July, 2015.

  Details about Why Multi−Objective Reinforcement Learning? | BibTeX data for Why Multi−Objective Reinforcement Learning? | Download (pdf) of Why Multi−Objective Reinforcement Learning?

• [71]

  MergeRUCB: A Method for Large−Scale Online Ranker Evaluation

  Masrour Zoghi‚ Shimon Whiteson and Maarten de Rijke

  In WSDM 2015: Proceedings of the Eighth ACM International Conference on Web Search and Data Mining. Pages 17−26. February, 2015.

  Details about MergeRUCB: A Method for Large−Scale Online Ranker Evaluation | BibTeX data for MergeRUCB: A Method for Large−Scale Online Ranker Evaluation | Download (pdf) of MergeRUCB: A Method for Large−Scale Online Ranker Evaluation

• [72]

  Bayesian Ranker Comparison Based on Historical User Interactions

  Artem Grotov‚ Shimon Whiteson and Maarten de Rijke

  In SIGIR 2015: Proceedings of the Thirty−Eighth Annual ACM SIGIR Conference. Pages 273−282. August, 2015.

  Details about Bayesian Ranker Comparison Based on Historical User Interactions | BibTeX data for Bayesian Ranker Comparison Based on Historical User Interactions | Download (pdf) of Bayesian Ranker Comparison Based on Historical User Interactions

• [73]

  Exploiting Submodular Value Functions for Faster Dynamic Sensor Selection

  Yash Satsangi‚ Shimon Whiteson and Frans Oliehoek

  In AAAI 2015: Proceedings of the Twenty−Ninth AAAI Conference on Artificial Intelligence. Pages 3356−3363. January, 2015.

  Details about Exploiting Submodular Value Functions for Faster Dynamic Sensor Selection | BibTeX data for Exploiting Submodular Value Functions for Faster Dynamic Sensor Selection | Download (pdf) of Exploiting Submodular Value Functions for Faster Dynamic Sensor Selection

• [74]

  Point−Based Planning for Multi−Objective POMDPs

  Diederik Roijers‚ Shimon Whiteson and Frans Oliehoek

  In IJCAI 2015: Proceedings of the Twenty−Fourth International Joint Conference on Artificial Intelligence. Pages 1666−1672. July, 2015.

  Details about Point−Based Planning for Multi−Objective POMDPs | BibTeX data for Point−Based Planning for Multi−Objective POMDPs | Download (pdf) of Point−Based Planning for Multi−Objective POMDPs

• [75]

  Leveraging Social Networks to Motivate Humans to Train Agents

  Guangliang Li‚ Hayley Hung‚ Shimon Whiteson and W. Bradley Knox

  In AAMAS 2014: Proceedings of the Thirteenth International Joint Conference on Autonomous Agents and Multi−Agent Systems. Pages 1571−1572. May, 2014.

  Extended Abstract.

  Details about Leveraging Social Networks to Motivate Humans to Train Agents | BibTeX data for Leveraging Social Networks to Motivate Humans to Train Agents | Download (pdf) of Leveraging Social Networks to Motivate Humans to Train Agents

• [76]

  Design Criteria for Challenge Balancing of Personalised Game Spaces

  Sander Bakkes and Shimon Whiteson

  In FDG 2014: Proceedings of the Ninth International Conference on the Foundations of Digital Games. April, 2014.

  Short paper.

  Details about Design Criteria for Challenge Balancing of Personalised Game Spaces | BibTeX data for Design Criteria for Challenge Balancing of Personalised Game Spaces | Download (pdf) of Design Criteria for Challenge Balancing of Personalised Game Spaces

• [77]

  Towards Challenge Balancing for Personalised Game Spaces

  Sander Bakkes and Shimon Whiteson

  In FDG 2014: Proceedings of the Ninth International Conference on the Foundations of Digital Games‚ Workshop on Procedural Content Generation in Games. April, 2014.

  Details about Towards Challenge Balancing for Personalised Game Spaces | BibTeX data for Towards Challenge Balancing for Personalised Game Spaces | Download (pdf) of Towards Challenge Balancing for Personalised Game Spaces

• [78]

  Bounded Approximations for Linear Multi−Objective Planning under Uncertainty

  Diederik Roijers‚ Joris Scharpff‚ Matthijs Spaan‚ Frans Oliehoek‚ Mathijs De Weerdt and Shimon Whiteson

  In BNAIC 2014: Proceedings of the Twenty−Sixth Benelux Conference on Artificial Intelligence. Pages 168−169. November, 2014.

  Details about Bounded Approximations for Linear Multi−Objective Planning under Uncertainty | BibTeX data for Bounded Approximations for Linear Multi−Objective Planning under Uncertainty | Download (pdf) of Bounded Approximations for Linear Multi−Objective Planning under Uncertainty

• [79]

  Optimizing Base Rankers Using Clicks: A Case Study using BM25

  Anne Schuth‚ Floor Sietsma‚ Shimon Whiteson and Maarten de Rijke

  In ECIR 2014: Proceedings of the Thirty−Sixth European Conference on Information Retrieval. Pages 75−87. April, 2014.

  Details about Optimizing Base Rankers Using Clicks: A Case Study using BM25 | BibTeX data for Optimizing Base Rankers Using Clicks: A Case Study using BM25 | Download (pdf) of Optimizing Base Rankers Using Clicks: A Case Study using BM25

• [80]

  Queued Pareto Local Search for Multi−Objective Optimization

  Maarten Inja‚ Chiel Kooijman‚ Maarten de Waard‚ Diederik Roijers and Shimon Whiteson

  In PPSN 2014: Proceedings of the Thirteenth International Conference on Parallel Problem Solving from Nature. Pages 589−599. September, 2014.

  Details about Queued Pareto Local Search for Multi−Objective Optimization | BibTeX data for Queued Pareto Local Search for Multi−Objective Optimization | Download (pdf) of Queued Pareto Local Search for Multi−Objective Optimization

• [81]

  Towards Personalised Gaming via Facial Expression Recognition

  Paris Mavromoustakos Blom‚ Sander Bakkes‚ Chek Tien Tan‚ Shimon Whiteson‚ Diederik Roijers‚ Roberto Valenti and Theo Gevers

  In AIIDE 2014: Proceedings of the Tenth Annual AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment. Pages 30−36. October, 2014.

  Details about Towards Personalised Gaming via Facial Expression Recognition | BibTeX data for Towards Personalised Gaming via Facial Expression Recognition | Download (pdf) of Towards Personalised Gaming via Facial Expression Recognition

• [82]

  Learning from Human Reward Benefits from Socio−competitive Feedback

  Guangliang Li‚ Hayley Hung‚ Shimon Whiteson and W. Bradley Knox

  In ICDL−Epirob 2014: Proceedings of the Fourth Joint IEEE International Conference on Development and Learning and on Epigenetic Robotics. Pages 70−77. October, 2014.

  Details about Learning from Human Reward Benefits from Socio−competitive Feedback | BibTeX data for Learning from Human Reward Benefits from Socio−competitive Feedback | Download (pdf) of Learning from Human Reward Benefits from Socio−competitive Feedback

• [83]

  Challenge Balancing for Personalised Game Spaces

  Sander Bakkes‚ Shimon Whiteson‚ Guangliang Li‚ George Viorel Visniuc‚ Efstathios Charitos‚ Norbert Heijne and Arjen Swellengrebel

  In GEM 2014: Proceedings of the IEEE Games‚ Entertainment‚ and Media Conference. Pages 38−45. October, 2014.

  Details about Challenge Balancing for Personalised Game Spaces | BibTeX data for Challenge Balancing for Personalised Game Spaces | Download (pdf) of Challenge Balancing for Personalised Game Spaces

• [84]

  Using Confidence Bounds for Efficient On−Line Ranker Evaluation

  Masrour Zoghi‚ Shimon Whiteson‚ Maarten de Rijke and Remi Munos

  In WSDM 2014: Proceedings of the Seventh ACM International Conference on Web Search and Data Mining. Pages 73−82. February, 2014.

  Details about Using Confidence Bounds for Efficient On−Line Ranker Evaluation | BibTeX data for Using Confidence Bounds for Efficient On−Line Ranker Evaluation | Download (pdf) of Using Confidence Bounds for Efficient On−Line Ranker Evaluation

• [85]

  Multileaved Comparisons for Fast Online Evaluation

  Anne Schuth‚ Floor Sietsma‚ Shimon Whiteson‚ Damien Lefortier and Maarten de Rijke

  In CIKM 2014: Proceedings of the Twenty−Third Conference on Information and Knowledge Management. Pages 71−80. November, 2014.

  Details about Multileaved Comparisons for Fast Online Evaluation | BibTeX data for Multileaved Comparisons for Fast Online Evaluation | Download (pdf) of Multileaved Comparisons for Fast Online Evaluation

• [86]

  Relative Upper Confidence Bound for the K−Armed Dueling Bandit Problem

  Masrour Zoghi‚ Shimon Whiteson‚ Remi Munos and Maarten de Rijke

  In ICML 2014: Proceedings of the Thirty−First International Conference on Machine Learning. Pages 10−18. June, 2014.

  Details about Relative Upper Confidence Bound for the K−Armed Dueling Bandit Problem | BibTeX data for Relative Upper Confidence Bound for the K−Armed Dueling Bandit Problem | Download (pdf) of Relative Upper Confidence Bound for the K−Armed Dueling Bandit Problem

• [87]

  Linear Support for Multi−Objective Coordination Graphs

  Diederik Roijers‚ Shimon Whiteson and Frans Oliehoek

  In AAMAS 2014: Proceedings of the Thirteenth International Joint Conference on Autonomous Agents and Multi−Agent Systems. Pages 1297−1304. May, 2014.

  Details about Linear Support for Multi−Objective Coordination Graphs | BibTeX data for Linear Support for Multi−Objective Coordination Graphs | Download (pdf) of Linear Support for Multi−Objective Coordination Graphs

• [88]

  Bounded Approximations for Linear Multi−Objective Planning under Uncertainty

  Diederik Roijers‚ Joris Scharpff‚ Matthijs Spaan‚ Frans Oliehoek‚ Mathijs De Weerdt and Shimon Whiteson

  In ICAPS 2014: Proceedings of the Twenty−Fourth International Conference on Automated Planning and Scheduling. Pages 262−270. June, 2014.

  Details about Bounded Approximations for Linear Multi−Objective Planning under Uncertainty | BibTeX data for Bounded Approximations for Linear Multi−Objective Planning under Uncertainty | Download (pdf) of Bounded Approximations for Linear Multi−Objective Planning under Uncertainty

• [89]

  Multi−Objective Variable Elimination for Collaborative Graphical Games

  Diederik Roijers‚ Shimon Whiteson and Frans Oliehoek

  In AAMAS 2013: Proceedings of the Twelfth International Joint Conference on Autonomous Agents and Multi−Agent Systems. Pages 1209−1210. May, 2013.

  Extended Abstract.

  Details about Multi−Objective Variable Elimination for Collaborative Graphical Games | BibTeX data for Multi−Objective Variable Elimination for Collaborative Graphical Games | Download (pdf) of Multi−Objective Variable Elimination for Collaborative Graphical Games

• [90]

  Efficient Abstraction Selection in Reinforcement Learning

  Harm van Seijen‚ Shimon Whiteson and Leon Kester

  In SARA 2013: Proceedings of the Tenth Symposium on Abstraction‚ Reformulation‚ and Approximation. Pages 123−127. July, 2013.

  Extended Abstract.

  Details about Efficient Abstraction Selection in Reinforcement Learning | BibTeX data for Efficient Abstraction Selection in Reinforcement Learning | Download (pdf) of Efficient Abstraction Selection in Reinforcement Learning

• [91]

  Lerot: an Online Learning to Rank Framework

  Anne Schuth‚ Katja Hofmann‚ Shimon Whiteson and Maarten de Rijke

  In CIKM 2013: Proceedings of the Twenty−Second Conference on Information and Knowledge Management‚ Workshop on Living Labs for Information Retrieval Evaluation. Pages 23−26. November, 2013.

  Details about Lerot: an Online Learning to Rank Framework | BibTeX data for Lerot: an Online Learning to Rank Framework | Download (pdf) of Lerot: an Online Learning to Rank Framework

• [92]

  Approximate Solutions for Factored Dec−POMDPs with Many Agents – Extended Abstract

  Frans Oliehoek‚ Shimon Whiteson and Matthijs Spaan

  In BNAIC 2013: Proceedings of the Twenty−Fifth Benelux Conference on Artificial Intelligence. November, 2013.

  Details about Approximate Solutions for Factored Dec−POMDPs with Many Agents – Extended Abstract | BibTeX data for Approximate Solutions for Factored Dec−POMDPs with Many Agents – Extended Abstract | Download (pdf) of Approximate Solutions for Factored Dec−POMDPs with Many Agents – Extended Abstract

• [93]

  Critical Factors in the Performance of HyperNEAT

  Thomas van den Berg and Shimon Whiteson

  In GECCO 2013: Proceedings of the Genetic and Evolutionary Computation Conference. Pages 759−766. July, 2013.

  Details about Critical Factors in the Performance of HyperNEAT | BibTeX data for Critical Factors in the Performance of HyperNEAT | Download (pdf) of Critical Factors in the Performance of HyperNEAT

• [94]

  Computing Convex Coverage Sets for Multi−Objective Coordination Graphs

  Diederik Roijers‚ Shimon Whiteson and Frans Oliehoek

  In ADT 2013: Proceedings of the Third International Conference on Algorithmic Decision Theory. Pages 309−323. November, 2013.

  Details about Computing Convex Coverage Sets for Multi−Objective Coordination Graphs | BibTeX data for Computing Convex Coverage Sets for Multi−Objective Coordination Graphs | Download (pdf) of Computing Convex Coverage Sets for Multi−Objective Coordination Graphs

• [95]

  Reusing Historical Interaction Data for Faster Online Learning to Rank for IR

  Katja Hofmann‚ Anne Schuth‚ Shimon Whiteson and Maarten de Rijke

  In WSDM 2013: Proceedings of the Sixth ACM International Conference on Web Search and Data Mining. Pages 183−192. February, 2013.

  Details about Reusing Historical Interaction Data for Faster Online Learning to Rank for IR | BibTeX data for Reusing Historical Interaction Data for Faster Online Learning to Rank for IR | Download (pdf) of Reusing Historical Interaction Data for Faster Online Learning to Rank for IR

• [96]

  Approximate Solutions for Factored Dec−POMDPs with Many Agents

  Frans Oliehoek‚ Shimon Whiteson and Matthijs Spaan

  In AAMAS 2013: Proceedings of the Twelfth International Joint Conference on Autonomous Agents and Multi−Agent Systems. Pages 563−570. May, 2013.

  Details about Approximate Solutions for Factored Dec−POMDPs with Many Agents | BibTeX data for Approximate Solutions for Factored Dec−POMDPs with Many Agents | Download (pdf) of Approximate Solutions for Factored Dec−POMDPs with Many Agents

• [97]

  Using Informative Behavior to Increase Engagement in the TAMER Framework

  Guangliang Li‚ Hayley Hung‚ Shimon Whiteson and W. Bradley Knox

  In AAMAS 2013: Proceedings of the Twelfth International Joint Conference on Autonomous Agents and Multi−Agent Systems. Pages 909−916. May, 2013.

  Details about Using Informative Behavior to Increase Engagement in the TAMER Framework | BibTeX data for Using Informative Behavior to Increase Engagement in the TAMER Framework | Download (pdf) of Using Informative Behavior to Increase Engagement in the TAMER Framework

• [98]

  Estimating Interleaved Comparison Outcomes from Historical Click Data

  Katja Hofmann‚ Shimon Whiteson and Maarten de Rijke

  In CIKM 2012: Proceedings of the Twenty−First Conference on Information and Knowledge Management. Pages 1779−1783. October, 2012.

  Short paper.

  Details about Estimating Interleaved Comparison Outcomes from Historical Click Data | BibTeX data for Estimating Interleaved Comparison Outcomes from Historical Click Data | Download (pdf) of Estimating Interleaved Comparison Outcomes from Historical Click Data

• [99]

  Exploiting Structure in Cooperative Bayesian Games

  Frans Oliehoek‚ Shimon Whiteson and Matthijs Spaan

  In UAI 2012: Proceedings of the Twenty−Eighth Conference on Uncertainty in Artificial Intelligence. Pages 654−664. August, 2012.

  Details about Exploiting Structure in Cooperative Bayesian Games | BibTeX data for Exploiting Structure in Cooperative Bayesian Games | Download (pdf) of Exploiting Structure in Cooperative Bayesian Games

• [100]

  V−MAX: Tempered Optimism for Better PAC Reinforcement Learning

  Karun Rao and Shimon Whiteson

  In AAMAS 2012: Proceedings of the Eleventh International Joint Conference on Autonomous Agents and Multi−Agent Systems. Pages 375−382. June, 2012.

  Details about V−MAX: Tempered Optimism for Better PAC Reinforcement Learning | BibTeX data for V−MAX: Tempered Optimism for Better PAC Reinforcement Learning | Download (pdf) of V−MAX: Tempered Optimism for Better PAC Reinforcement Learning

• [101]

  Multi−Task Reinforcement Learning: Shaping and Feature Selection

  Matthijs Snel and Shimon Whiteson

  In EWRL 2011: Proceedings of the Ninth European Workshop on Reinforcement Learning. September, 2011.

  Details about Multi−Task Reinforcement Learning: Shaping and Feature Selection | BibTeX data for Multi−Task Reinforcement Learning: Shaping and Feature Selection | Download (pdf) of Multi−Task Reinforcement Learning: Shaping and Feature Selection

• [102]

  Contextual Bandits for Information Retrieval

  Katja Hofmann‚ Shimon Whiteson and Maarten de Rijke

  In NeurIPS 2011: Proceedings of the Conference on Neural Information Processing Systems‚ Workshop on Bayesian Optimization‚ Experimental Design and Bandits: Theory and Applications. December, 2011.

  Details about Contextual Bandits for Information Retrieval | BibTeX data for Contextual Bandits for Information Retrieval | Download (pdf) of Contextual Bandits for Information Retrieval

• [103]

  Critical Factors in the Performance of Novelty Search

  Steijn Kistemaker and Shimon Whiteson

  In GECCO 2011: Proceedings of the Genetic and Evolutionary Computation Conference. Pages 965−972. July, 2011.

  Details about Critical Factors in the Performance of Novelty Search | BibTeX data for Critical Factors in the Performance of Novelty Search | Download (pdf) of Critical Factors in the Performance of Novelty Search

• [104]

  Protecting Against Evaluation Overfitting in Empirical Reinforcement Learning

  Shimon Whiteson‚ Brian Tanner‚ Matthew E. Taylor and Peter Stone

  In ADPRL 2011: Proceedings of the IEEE Symposium on Adaptive Dynamic Programming and Reinforcement Learning. Pages 120−127. April, 2011.

  Details about Protecting Against Evaluation Overfitting in Empirical Reinforcement Learning | BibTeX data for Protecting Against Evaluation Overfitting in Empirical Reinforcement Learning | Download (pdf) of Protecting Against Evaluation Overfitting in Empirical Reinforcement Learning

• [105]

  Balancing Exploration and Exploitation in Learning to Rank Online

  Katja Hofmann‚ Shimon Whiteson and Maarten de Rijke

  In ECIR 2011: Proceedings of the Thirty−Third European Conference on Information Retrieval. Pages 251−263. April, 2011.

  Details about Balancing Exploration and Exploitation in Learning to Rank Online | BibTeX data for Balancing Exploration and Exploitation in Learning to Rank Online | Download (pdf) of Balancing Exploration and Exploitation in Learning to Rank Online

• [106]

  Robust Central Pattern Generators for Embodied Hierarchical Reinforcement Learning

  Matthijs Snel‚ Shimon Whiteson and Yasuo Kuniyoshi

  In ICDL−Epirob 2011: Proceedings of the First Joint IEEE International Conference on Development and Learning and on Epigenetic Robotics. Pages 1−6. August, 2011.

  Details about Robust Central Pattern Generators for Embodied Hierarchical Reinforcement Learning | BibTeX data for Robust Central Pattern Generators for Embodied Hierarchical Reinforcement Learning | Download (pdf) of Robust Central Pattern Generators for Embodied Hierarchical Reinforcement Learning

• [107]

  A Probabilistic Method for Inferring Preferences from Clicks

  Katja Hofmann‚ Shimon Whiteson and Maarten de Rijke

  In CIKM 2011: Proceedings of the Twentieth Conference on Information and Knowledge Management. Pages 249−258. October, 2011.

  Details about A Probabilistic Method for Inferring Preferences from Clicks | BibTeX data for A Probabilistic Method for Inferring Preferences from Clicks | Download (pdf) of A Probabilistic Method for Inferring Preferences from Clicks

• [108]

  Multi−Task Evolutionary Shaping Without Pre−Specified Representations

  Matthijs Snel and Shimon Whiteson

  In GECCO 2010: Proceedings of the Genetic and Evolutionary Computation Conference. Pages 1031−1038. July, 2010.

  Details about Multi−Task Evolutionary Shaping Without Pre−Specified Representations | BibTeX data for Multi−Task Evolutionary Shaping Without Pre−Specified Representations | Download (pdf) of Multi−Task Evolutionary Shaping Without Pre−Specified Representations

• [109]

  Generalized Domains for Empirical Evaluations in Reinforcement Learning

  Shimon Whiteson‚ Brian Tanner‚ Matthew E. Taylor and Peter Stone

  In ICML 2009: Proceedings of the Twenty−Sixth International Conference on Machine Learning: Workshop on Evaluation Methods for Machine Learning. June, 2009.

  Details about Generalized Domains for Empirical Evaluations in Reinforcement Learning | BibTeX data for Generalized Domains for Empirical Evaluations in Reinforcement Learning | Download (pdf) of Generalized Domains for Empirical Evaluations in Reinforcement Learning

• [110]

  Neuroevolutionary Reinforcement Learning for Generalized Helicopter Control

  Rogier Koppejan and Shimon Whiteson

  In GECCO 2009: Proceedings of the Genetic and Evolutionary Computation Conference. Pages 145−152. July, 2009.

  Details about Neuroevolutionary Reinforcement Learning for Generalized Helicopter Control | BibTeX data for Neuroevolutionary Reinforcement Learning for Generalized Helicopter Control | Download (pdf) of Neuroevolutionary Reinforcement Learning for Generalized Helicopter Control

• [111]

  A Theoretical and Empirical Analysis of Expected Sarsa

  Harm van Seijen‚ Hado van Hasselt‚ Shimon Whiteson and Marco Wiering

  In ADPRL 2009: Proceedings of the IEEE Symposium on Adaptive Dynamic Programming and Reinforcement Learning. Pages 177−184. March, 2009.

  Details about A Theoretical and Empirical Analysis of Expected Sarsa | BibTeX data for A Theoretical and Empirical Analysis of Expected Sarsa | Download (pdf) of A Theoretical and Empirical Analysis of Expected Sarsa

• [112]

  Integrating Distributed Bayesian Inference and Reinforcement Learning for Sensor Management

  Corrado Grappiolo‚ Shimon Whiteson‚ Gregor Pavlin and Bram Bakker

  In FUSION 2009: Proceedings of the Twelfth International Conference on Information Fusion. Pages 93−101. July, 2009.

  Details about Integrating Distributed Bayesian Inference and Reinforcement Learning for Sensor Management | BibTeX data for Integrating Distributed Bayesian Inference and Reinforcement Learning for Sensor Management | Download (pdf) of Integrating Distributed Bayesian Inference and Reinforcement Learning for Sensor Management

• [113]

  Postponed Updates for Temporal−Difference Reinforcement Learning

  Harm van Seijen and Shimon Whiteson

  In ISDA 2009: Proceedings of the Ninth International Conference on Intelligent Systems Design and Applications. Pages 665−672. November, 2009.

  Details about Postponed Updates for Temporal−Difference Reinforcement Learning | BibTeX data for Postponed Updates for Temporal−Difference Reinforcement Learning | Download (pdf) of Postponed Updates for Temporal−Difference Reinforcement Learning

• [114]

  Automatic Feature Selection for Model−Based Reinforcement Learning in Factored MDPs

  Mark Kroon and Shimon Whiteson

  In ICMLA 2009: Proceedings of the Eighth International Conference on Machine Learning and Applications. Pages 324−330. December, 2009.

  Details about Automatic Feature Selection for Model−Based Reinforcement Learning in Factored MDPs | BibTeX data for Automatic Feature Selection for Model−Based Reinforcement Learning in Factored MDPs | Download (pdf) of Automatic Feature Selection for Model−Based Reinforcement Learning in Factored MDPs

• [115]

  Lossless Clustering of Histories in Decentralized POMDPs

  Frans Oliehoek‚ Shimon Whiteson and Matthijs Spaan

  In AAMAS 2009: Proceedings of the Eighth International Joint Conference on Autonomous Agents and Multi−Agent Systems. Pages 577−584. May, 2009.

  Details about Lossless Clustering of Histories in Decentralized POMDPs | BibTeX data for Lossless Clustering of Histories in Decentralized POMDPs | Download (pdf) of Lossless Clustering of Histories in Decentralized POMDPs

• [116]

  Multiagent Reinforcement Learning for Urban Traffic Control using Coordination Graphs

  Lior Kuyer‚ Shimon Whiteson‚ Bram Bakker and Nikos Vlassis

  In ECML 2008: Proceedings of the Nineteenth European Conference on Machine Learning. Pages 656−671. September, 2008.

  Details about Multiagent Reinforcement Learning for Urban Traffic Control using Coordination Graphs | BibTeX data for Multiagent Reinforcement Learning for Urban Traffic Control using Coordination Graphs | Download (pdf) of Multiagent Reinforcement Learning for Urban Traffic Control using Coordination Graphs

• [117]

  Exploiting Locality of Interaction in Factored Dec−POMDPs

  Frans Oliehoek‚ Matthijs Spaan‚ Shimon Whiteson and Nikos Vlassis

  In AAMAS 2008: Proceedings of the Seventh International Joint Conference on Autonomous Agents and Multi−Agent Systems. Pages 517−524. May, 2008.

  Details about Exploiting Locality of Interaction in Factored Dec−POMDPs | BibTeX data for Exploiting Locality of Interaction in Factored Dec−POMDPs | Download (pdf) of Exploiting Locality of Interaction in Factored Dec−POMDPs

• [118]

  Temporal Difference and Policy Search Methods for Reinforcement Learning: An Empirical Comparison

  Matthew E. Taylor‚ Shimon Whiteson and Peter Stone

  In AAAI 2007: Proceedings of the Twenty−Second National Conference on Artificial Intelligence. Pages 1675−1678. July, 2007.

  (Nectar Track)

  Details about Temporal Difference and Policy Search Methods for Reinforcement Learning: An Empirical Comparison | BibTeX data for Temporal Difference and Policy Search Methods for Reinforcement Learning: An Empirical Comparison | Download (pdf) of Temporal Difference and Policy Search Methods for Reinforcement Learning: An Empirical Comparison

• [119]

  Stochastic Optimization for Collision Selection in High Energy Physics

  Shimon Whiteson and Daniel Whiteson

  In IAAI 2007: Proceedings of the Nineteenth Annual Innovative Applications of Artificial Intelligence Conference. Pages 1819−1825. July, 2007.

  Details about Stochastic Optimization for Collision Selection in High Energy Physics | BibTeX data for Stochastic Optimization for Collision Selection in High Energy Physics | Download (pdf) of Stochastic Optimization for Collision Selection in High Energy Physics

• [120]

  Transfer via Inter−Task Mappings in Policy Search Reinforcement Learning

  Matthew E. Taylor‚ Shimon Whiteson and Peter Stone

  In AAMAS 2007: Proceedings of the Sixth International Joint Conference on Autonomous Agents and Multi−Agent Systems. Pages 156−163. May, 2007.

  Details about Transfer via Inter−Task Mappings in Policy Search Reinforcement Learning | BibTeX data for Transfer via Inter−Task Mappings in Policy Search Reinforcement Learning | Download (pdf) of Transfer via Inter−Task Mappings in Policy Search Reinforcement Learning

• [121]

  Evolutionary Function Approximation for Reinforcement Learning

  Shimon Whiteson

  In GECCO 2006: Proceedings of the Genetic and Evolutionary Computation Conference Graduate Student Workshop. July, 2006.

  Best Paper Award‚ Graduate Student Workshop.

  Details about Evolutionary Function Approximation for Reinforcement Learning | BibTeX data for Evolutionary Function Approximation for Reinforcement Learning | Download (pdf) of Evolutionary Function Approximation for Reinforcement Learning

• [122]

  Transfer Learning for Policy Search Methods

  Matthew E. Taylor‚ Shimon Whiteson and Peter Stone

  In ICML 2006: Proceedings of the Twenty−Third International Conference on Machine Learning Transfer Learning Workshop. June, 2006.

  Details about Transfer Learning for Policy Search Methods | BibTeX data for Transfer Learning for Policy Search Methods | Download (pdf) of Transfer Learning for Policy Search Methods

• [123]

  Comparing Evolutionary and Temporal Difference Methods in a Reinforcement Learning Domain

  Matthew E. Taylor‚ Shimon Whiteson and Peter Stone

  In GECCO 2006: Proceedings of the Genetic and Evolutionary Computation Conference. Pages 1321−1328. July, 2006.

  Best Paper Award‚ Genetic Algorithms Track.

  Details about Comparing Evolutionary and Temporal Difference Methods in a Reinforcement Learning Domain | BibTeX data for Comparing Evolutionary and Temporal Difference Methods in a Reinforcement Learning Domain | Download (pdf) of Comparing Evolutionary and Temporal Difference Methods in a Reinforcement Learning Domain

• [124]

  On−Line Evolutionary Computation for Reinforcement Learning in Stochastic Domains

  Shimon Whiteson and Peter Stone

  In GECCO 2006: Proceedings of the Genetic and Evolutionary Computation Conference. Pages 1577−1584. July, 2006.

  Details about On−Line Evolutionary Computation for Reinforcement Learning in Stochastic Domains | BibTeX data for On−Line Evolutionary Computation for Reinforcement Learning in Stochastic Domains | Download (pdf) of On−Line Evolutionary Computation for Reinforcement Learning in Stochastic Domains

• [125]

  Sample−Efficient Evolutionary Function Approximation for Reinforcement Learning

  Shimon Whiteson and Peter Stone

  In AAAI 2006: Proceedings of the Twenty−First National Conference on Artificial Intelligence. Pages 518−523. July, 2006.

  Details about Sample−Efficient Evolutionary Function Approximation for Reinforcement Learning | BibTeX data for Sample−Efficient Evolutionary Function Approximation for Reinforcement Learning | Download (pdf) of Sample−Efficient Evolutionary Function Approximation for Reinforcement Learning

• [126]

  Improving Reinforcement Learning Function Approximators via Neuroevolution

  Shimon Whiteson

  In AAAI 2005: Tenth Annual Doctoral Consortium. July, 2005.

  Details about Improving Reinforcement Learning Function Approximators via Neuroevolution | BibTeX data for Improving Reinforcement Learning Function Approximators via Neuroevolution | Download (pdf) of Improving Reinforcement Learning Function Approximators via Neuroevolution

• [127]

  Automatic Feature Selection in Neuroevolution

  Shimon Whiteson‚ Peter Stone‚ Kenneth O. Stanley‚ Risto Miikkulainen and Nate Kohl

  In GECCO 2005: Proceedings of the Genetic and Evolutionary Computation Conference. Pages 1225−1232. June, 2005.

  Details about Automatic Feature Selection in Neuroevolution | BibTeX data for Automatic Feature Selection in Neuroevolution | Download (pdf) of Automatic Feature Selection in Neuroevolution

• [128]

  Automatic Feature Selection in Neuroevolution

  Shimon Whiteson‚ Kenneth O. Stanley and Risto Miikkulainen

  In GECCO 2004: Proceedings of the Genetic and Evolutionary Computation Conference Workshop on Self−Organization. July, 2004.

  Details about Automatic Feature Selection in Neuroevolution | BibTeX data for Automatic Feature Selection in Neuroevolution | Download (pdf) of Automatic Feature Selection in Neuroevolution

• [129]

  Towards Autonomic Computing: Adaptive Job Routing and Scheduling

  Shimon Whiteson and Peter Stone

  In IAAI 2004: Proceedings of the Sixteenth Annual Innovative Applications of Artificial Intelligence Conference. Pages 916−922. July, 2004.

  Details about Towards Autonomic Computing: Adaptive Job Routing and Scheduling | BibTeX data for Towards Autonomic Computing: Adaptive Job Routing and Scheduling | Download (pdf) of Towards Autonomic Computing: Adaptive Job Routing and Scheduling

• [130]

  Evolving RoboCup Keepaway Players through Task Decomposition

  Shimon Whiteson‚ Nate Kohl‚ Risto Miikkulainen and Peter Stone

  In GECCO 2003: Proceedings of the Genetic and Evolutionary Computation Conference. Pages 356−368. July, 2003.

  Details about Evolving RoboCup Keepaway Players through Task Decomposition | BibTeX data for Evolving RoboCup Keepaway Players through Task Decomposition | Download (pdf) of Evolving RoboCup Keepaway Players through Task Decomposition

• [131]

  Concurrent Layered Learning

  Shimon Whiteson and Peter Stone

  In AAMAS 2003: Proceedings of the Second International Joint Conference on Autonomous Agents and Multi−Agent Systems. Pages 193−200. July, 2003.

  Details about Concurrent Layered Learning | BibTeX data for Concurrent Layered Learning | Download (pdf) of Concurrent Layered Learning

Technical reports

• [1]

  An Analysis of Piecewise−Linear and Convex Value Functions for Active Perception POMDPs

  Yash Satsangi‚ Shimon Whiteson and Matthijs T. J. Spaan

  No. IAS−UVA−15−01. University of Amsterdam‚ Informatics Institute. 2015.

  Details about An Analysis of Piecewise−Linear and Convex Value Functions for Active Perception POMDPs | BibTeX data for An Analysis of Piecewise−Linear and Convex Value Functions for Active Perception POMDPs | Download (pdf) of An Analysis of Piecewise−Linear and Convex Value Functions for Active Perception POMDPs

• [2]

  Exploiting Submodular Value Functions for Faster Dynamic Sensor Selection: Extended Version

  Yash Satsangi‚ Shimon Whiteson and Frans Oliehoek

  No. IAS− UVA−14−02. University of Amsterdam‚ Informatics Institute. 2014.

  Details about Exploiting Submodular Value Functions for Faster Dynamic Sensor Selection: Extended Version | BibTeX data for Exploiting Submodular Value Functions for Faster Dynamic Sensor Selection: Extended Version | Download (pdf) of Exploiting Submodular Value Functions for Faster Dynamic Sensor Selection: Extended Version

• [3]

  Automatic Feature Selection using FS−NEAT

  Aksel Ethembabaoglu and Shimon Whiteson

  No. IAS−UVA−08−02. Intelligent Autonomous Systems Group‚ University of Amsterdam. 2008.

  Details about Automatic Feature Selection using FS−NEAT | BibTeX data for Automatic Feature Selection using FS−NEAT | Download (pdf) of Automatic Feature Selection using FS−NEAT

• [4]

  Adaptive Tile Coding for Value Function Approximation

  Shimon Whiteson‚ Matthew E. Taylor and Peter Stone

  No. AI−TR−07−339. University of Texas at Austin. 2007.

  Details about Adaptive Tile Coding for Value Function Approximation | BibTeX data for Adaptive Tile Coding for Value Function Approximation | Download (pdf) of Adaptive Tile Coding for Value Function Approximation

Theses

• [1]

  Adaptive Representations for Reinforcement Learning

  Shimon Whiteson

  PhD Thesis Department of Computer Science‚ University of Texas at Austin. 2007.

  Details about Adaptive Representations for Reinforcement Learning | BibTeX data for Adaptive Representations for Reinforcement Learning | Download (pdf) of Adaptive Representations for Reinforcement Learning