Peter Sunehag - RankMe

59 papers A* 4A 5B 5C 3Misc 1Journal 25Unranked 16

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	A Theory of Appropriateness That Accounts for Norms of Rationality. CoRR Joel Z. Leibo, Alexander Sasha Vezhnevets, Manfred Diaz, John P. Agapiou, William A. Cunningham, Peter Sunehag, Logan Cross, Raphael Koster, Stanley M. Bileschi, Minsuk Chang, Iyad Rahwan, Simon Osindero, James A. Evans
2025	J	`jnl`	Simulation Streams: A Programming Paradigm for Controlling Large Language Models and Building Complex Systems with Generative AI. CoRR Peter Sunehag, Joel Z. Leibo
2024	J	`jnl`	A theory of appropriateness with applications to generative artificial intelligence. CoRR Joel Z. Leibo, Alexander Sasha Vezhnevets, Manfred Diaz, John P. Agapiou, William A. Cunningham, Peter Sunehag, Julia Haas, Raphael Koster, Edgar A. Duéñez-Guzmán, William S. Isaac, Georgios Piliouras, Stanley M. Bileschi, Iyad Rahwan, Simon Osindero
2023	J	`jnl`	A Review of Cooperation in Multi-agent Learning. CoRR Yali Du, Joel Z. Leibo, Usman Islam, Richard Willis, Peter Sunehag
2023	A	`conf`	Diversity Through Exclusion (DTE): Niche Identification for Reinforcement Learning through Value-Decomposition. AAMAS Peter Sunehag, Alexander Sasha Vezhnevets, Edgar A. Duéñez-Guzmán, Igor Mordatch, Joel Z. Leibo
2023	J	`jnl`	Diversity Through Exclusion (DTE): Niche Identification for Reinforcement Learning through Value-Decomposition. CoRR Peter Sunehag, Alexander Sasha Vezhnevets, Edgar A. Duéñez-Guzmán, Igor Mordatch, Joel Z. Leibo
2022	J	`jnl`	Melting Pot 2.0. CoRR John P. Agapiou, Alexander Sasha Vezhnevets, Edgar A. Duéñez-Guzmán, Jayd Matyas, Yiran Mao, Peter Sunehag, Raphael Köster, Udari Madhushani, Kavya Kopparapu, Ramona Comanescu, DJ Strouse, Michael Bradley Johanson, Sukhdeep Singh, Julia Haas, Igor Mordatch, Dean Mobbs, Joel Z. Leibo
2021	A*	`conf`	Scalable Evaluation of Multi-Agent Reinforcement Learning with Melting Pot. ICML Joel Z. Leibo, Edgar A. Duéñez-Guzmán, Alexander Vezhnevets, John P. Agapiou, Peter Sunehag, Raphael Koster, Jayd Matyas, Charlie Beattie, Igor Mordatch, Thore Graepel
2021	J	`jnl`	Scalable Evaluation of Multi-Agent Reinforcement Learning with Melting Pot. CoRR Joel Z. Leibo, Edgar A. Duéñez-Guzmán, Alexander Sasha Vezhnevets, John P. Agapiou, Peter Sunehag, Raphael Koster, Jayd Matyas, Charles Beattie, Igor Mordatch, Thore Graepel
2020	A*	`conf`	Learning to Incentivize Other Learning Agents. NeurIPS Jiachen Yang, Ang Li, Mehrdad Farajtabar, Peter Sunehag, Edward Hughes, Hongyuan Zha
2020	J	`jnl`	Learning to Incentivize Other Learning Agents. CoRR Jiachen Yang, Ang Li, Mehrdad Farajtabar, Peter Sunehag, Edward Hughes, Hongyuan Zha
2019	A	`conf`	Malthusian Reinforcement Learning. AAMAS Joel Z. Leibo, Julien Pérolat, Edward Hughes, Steven Wheelwright, Adam H. Marblestone, Edgar A. Duéñez-Guzmán, Peter Sunehag, Iain Dunning, Thore Graepel
2019	C	`conf`	Reinforcement Learning Agents acquire Flocking and Symbiotic Behaviour in Simulated Ecosystems. ALIFE Peter Sunehag, Guy Lever, Siqi Liu, Josh Merel, Nicolas Heess, Joel Z. Leibo, Edward Hughes, Tom Eccles, Thore Graepel
2018	J	`jnl`	Malthusian Reinforcement Learning. CoRR Joel Z. Leibo, Julien Pérolat, Edward Hughes, Steven Wheelwright, Adam H. Marblestone, Edgar A. Duéñez-Guzmán, Peter Sunehag, Iain Dunning, Thore Graepel
2018	A	`conf`	Value-Decomposition Networks For Cooperative Multi-Agent Learning Based On Team Reward. AAMAS Peter Sunehag, Guy Lever, Audrunas Gruslys, Wojciech Marian Czarnecki, Vinícius Flores Zambaldi, Max Jaderberg, Marc Lanctot, Nicolas Sonnerat, Joel Z. Leibo, Karl Tuyls, Thore Graepel
2017	J	`jnl`	Value-Decomposition Networks For Cooperative Multi-Agent Learning. CoRR Peter Sunehag, Guy Lever, Audrunas Gruslys, Wojciech Marian Czarnecki, Vinícius Flores Zambaldi, Max Jaderberg, Marc Lanctot, Nicolas Sonnerat, Joel Z. Leibo, Karl Tuyls, Thore Graepel
2015	J	`jnl`	Deep Reinforcement Learning with Attention for Slate Markov Decision Processes with High-Dimensional States and Actions. CoRR Peter Sunehag, Richard Evans, Gabriel Dulac-Arnold, Yori Zwols, Daniel Visentin, Ben Coppin
2015	J	`jnl`	Rationality, optimism and guarantees in general reinforcement learning. J. Mach. Learn. Res. Peter Sunehag, Marcus Hutter
2015	J	`jnl`	Reinforcement Learning in Large Discrete Action Spaces. CoRR Gabriel Dulac-Arnold, Richard Evans, Peter Sunehag, Ben Coppin
2015	—	`conf`	Using Localization and Factorization to Reduce the Complexity of Reinforcement Learning. AGI Peter Sunehag, Marcus Hutter
2014	B	`conf`	A Dual Process Theory of Optimistic Cognition. CogSci Peter Sunehag, Marcus Hutter
2014	—	`conf`	Intelligence as Inference or Forcing Occam on the World. AGI Peter Sunehag, Marcus Hutter
2014	C	`conf`	Reinforcement learning with value advice. ACML Mayank Daswani, Peter Sunehag, Marcus Hutter
2013	B	`conf`	Concentration and Confidence for Discrete Bayesian Sequence Predictors. ALT Tor Lattimore, Marcus Hutter, Peter Sunehag
2013	J	`jnl`	Concentration and Confidence for Discrete Bayesian Sequence Predictors. CoRR Tor Lattimore, Marcus Hutter, Peter Sunehag
2013	—	`conf`	Learning Agents with Evolving Hypothesis Classes. AGI Peter Sunehag, Marcus Hutter
2013	J	`jnl`	On Nicod's Condition, Rules of Induction and the Raven Paradox. CoRR Hadi Mohasel Afshar, Peter Sunehag
2013	—	`conf`	Online feature selection for Brain Computer Interfaces. CCMB Gareth Oliver, Peter Sunehag, Tom Gedeon
2013	C	`conf`	Q-learning for history-based reinforcement learning. ACML Mayank Daswani, Peter Sunehag, Marcus Hutter
2013	—	`conf`	The Sample-Complexity of General Reinforcement Learning. ICML (3) Tor Lattimore, Marcus Hutter, Peter Sunehag
2013	J	`jnl`	The Sample-Complexity of General Reinforcement Learning. CoRR Tor Lattimore, Marcus Hutter, Peter Sunehag
2012	J	`jnl`	Adaptive Context Tree Weighting CoRR Alexander O'Neill, Marcus Hutter, Wen Shao, Peter Sunehag
2012	B	`conf`	Adaptive Context Tree Weighting. DCC Alexander O'Neill, Marcus Hutter, Wen Shao, Peter Sunehag
2012	—	`conf`	Asynchronous Brain Computer Interface using Hidden Semi-Markov Models. EMBC Gareth Oliver, Peter Sunehag, Tom Gedeon
2012	Misc	`conf`	Coding of Non-Stationary Sources as a Foundation for Detecting Change Points and Outliers in Binary Time-Series. AusDM Peter Sunehag, Wen Shao, Marcus Hutter
2012	A*	`conf`	Context Tree Maximizing. AAAI Phuong Nguyen, Peter Sunehag, Marcus Hutter
2012	—	`conf`	Feature Reinforcement Learning using Looping Suffix Trees. EWRL Mayank Daswani, Peter Sunehag, Marcus Hutter
2012	—	`conf`	On Ensemble Techniques for AIXI Approximation. AGI Joel Veness, Peter Sunehag, Marcus Hutter
2012	—	`conf`	Optimistic AIXI. AGI Peter Sunehag, Marcus Hutter
2012	—	`conf`	Optimistic Agents Are Asymptotically Optimal. Australasian Conference on Artificial Intelligence Peter Sunehag, Marcus Hutter
2012	J	`jnl`	Optimistic Agents are Asymptotically Optimal CoRR Peter Sunehag, Marcus Hutter
2012	—	`conf`	Recursive channel selection techniques for brain computer interfaces. EMBC Gareth Oliver, Peter Sunehag, Tom Gedeon
2011	J	`jnl`	(Non-)Equivalence of Universal Priors CoRR Ian Wood, Peter Sunehag, Marcus Hutter
2011	—	`conf`	(Non-)Equivalence of Universal Priors. Algorithmic Probability and Friends Ian Wood, Peter Sunehag, Marcus Hutter
2011	J	`jnl`	Axioms for Rational Reinforcement Learning CoRR Peter Sunehag, Marcus Hutter
2011	B	`conf`	Axioms for Rational Reinforcement Learning. ALT Peter Sunehag, Marcus Hutter
2011	J	`jnl`	Feature Reinforcement Learning In Practice CoRR Phuong Nguyen, Peter Sunehag, Marcus Hutter
2011	—	`conf`	Feature Reinforcement Learning in Practice. EWRL Phuong Nguyen, Peter Sunehag, Marcus Hutter
2011	—	`conf`	Gradient Based Algorithms with Loss Functions and Kernels for Improved On-Policy Control. EWRL Matthew W. Robards, Peter Sunehag
2011	J	`jnl`	Principles of Solomonoff Induction and AIXI CoRR Peter Sunehag, Marcus Hutter
2011	—	`conf`	Principles of Solomonoff Induction and AIXI. Algorithmic Probability and Friends Peter Sunehag, Marcus Hutter
2011	—	`conf`	Sparse Kernel-SARSA(λ) with an Eligibility Trace. ECML/PKDD (3) Matthew W. Robards, Peter Sunehag, Scott Sanner, Bhaskara Marthi
2010	J	`jnl`	Consistency of Feature Markov Processes CoRR Peter Sunehag, Marcus Hutter
2010	B	`conf`	Consistency of Feature Markov Processes. ALT Peter Sunehag, Marcus Hutter
2010	J	`jnl`	Wearable sensor activity analysis using semi-Markov models with a grammar. Pervasive Mob. Comput. Owen Thomas, Peter Sunehag, Gideon Dror, Sungrack Yun, Sungwoong Kim, Matthew W. Robards, Alexander J. Smola, Daniel Green, Philo Saunders
2009	A*	`conf`	Semi-Markov kMeans Clustering and Activity Recognition from Body-Worn Sensors. ICDM Matthew W. Robards, Peter Sunehag
2009	A	`conf`	Variable Metric Stochastic Approximation Theory. AISTATS Peter Sunehag, Jochen Trumpf, S. V. N. Vishwanathan, Nicol N. Schraudolph
2007	A	`conf`	Emerge and spread models and word burstiness. AISTATS Peter Sunehag
2004	J	`jnl`	Subcouples of codimension one and interpolation of operators that almost agree. J. Approx. Theory Peter Sunehag