Peter Sunehag

Publication Activity (10 Years)

Years Active: 2004-2023
Publications (10 Years): 13

Top Topics

Function Approximation

Markov Decision Process

Top Venues

Publications

Peter Sunehag, Alexander Sasha Vezhnevets, Edgar A. Duéñez-Guzmán, Igor Mordatch, Joel Z. Leibo
Diversity Through Exclusion (DTE): Niche Identification for Reinforcement Learning through Value-Decomposition. CoRR (2023)
Yali Du, Joel Z. Leibo, Usman Islam, Richard Willis, Peter Sunehag
A Review of Cooperation in Multi-agent Learning. CoRR (2023)
Peter Sunehag, Alexander Sasha Vezhnevets, Edgar A. Duéñez-Guzmán, Igor Mordatch, Joel Z. Leibo
Diversity Through Exclusion (DTE): Niche Identification for Reinforcement Learning through Value-Decomposition. AAMAS (2023)
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
Melting Pot 2.0. CoRR (2022)
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
Scalable Evaluation of Multi-Agent Reinforcement Learning with Melting Pot. CoRR (2021)
Joel Z. Leibo, Edgar A. Duéñez-Guzmán, Alexander Vezhnevets, John P. Agapiou, Peter Sunehag, Raphael Koster, Jayd Matyas, Charlie Beattie, Igor Mordatch, Thore Graepel
Scalable Evaluation of Multi-Agent Reinforcement Learning with Melting Pot. ICML (2021)
Jiachen Yang, Ang Li, Mehrdad Farajtabar, Peter Sunehag, Edward Hughes, Hongyuan Zha
Learning to Incentivize Other Learning Agents. CoRR (2020)
Jiachen Yang, Ang Li, Mehrdad Farajtabar, Peter Sunehag, Edward Hughes, Hongyuan Zha
Learning to Incentivize Other Learning Agents. NeurIPS (2020)
Peter Sunehag, Guy Lever, Siqi Liu, Josh Merel, Nicolas Heess, Joel Z. Leibo, Edward Hughes, Tom Eccles, Thore Graepel
Reinforcement Learning Agents acquire Flocking and Symbiotic Behaviour in Simulated Ecosystems. ALIFE (2019)
Joel Z. Leibo, Julien Pérolat, Edward Hughes, Steven Wheelwright, Adam H. Marblestone, Edgar A. Duéñez-Guzmán, Peter Sunehag, Iain Dunning, Thore Graepel
Malthusian Reinforcement Learning. AAMAS (2019)
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
Value-Decomposition Networks For Cooperative Multi-Agent Learning Based On Team Reward. AAMAS (2018)
Joel Z. Leibo, Julien Pérolat, Edward Hughes, Steven Wheelwright, Adam H. Marblestone, Edgar A. Duéñez-Guzmán, Peter Sunehag, Iain Dunning, Thore Graepel
Malthusian Reinforcement Learning. CoRR (2018)
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
Value-Decomposition Networks For Cooperative Multi-Agent Learning. CoRR (2017)
Gabriel Dulac-Arnold, Richard Evans, Peter Sunehag, Ben Coppin
Reinforcement Learning in Large Discrete Action Spaces. CoRR (2015)
Peter Sunehag, Marcus Hutter
Using Localization and Factorization to Reduce the Complexity of Reinforcement Learning. AGI (2015)
Peter Sunehag, Richard Evans, Gabriel Dulac-Arnold, Yori Zwols, Daniel Visentin, Ben Coppin
Deep Reinforcement Learning with Attention for Slate Markov Decision Processes with High-Dimensional States and Actions. CoRR (2015)
Peter Sunehag, Marcus Hutter
Rationality, optimism and guarantees in general reinforcement learning. J. Mach. Learn. Res. 16 (2015)
Peter Sunehag, Marcus Hutter
Intelligence as Inference or Forcing Occam on the World. AGI (2014)
Mayank Daswani, Peter Sunehag, Marcus Hutter
Reinforcement learning with value advice. ACML (2014)
Peter Sunehag, Marcus Hutter
A Dual Process Theory of Optimistic Cognition. CogSci (2014)
Mayank Daswani, Peter Sunehag, Marcus Hutter
Q-learning for history-based reinforcement learning. ACML (2013)
Tor Lattimore, Marcus Hutter, Peter Sunehag
The Sample-Complexity of General Reinforcement Learning. CoRR (2013)
Tor Lattimore, Marcus Hutter, Peter Sunehag
Concentration and Confidence for Discrete Bayesian Sequence Predictors. ALT (2013)
Tor Lattimore, Marcus Hutter, Peter Sunehag
The Sample-Complexity of General Reinforcement Learning. ICML (3) (2013)
Tor Lattimore, Marcus Hutter, Peter Sunehag
Concentration and Confidence for Discrete Bayesian Sequence Predictors. CoRR (2013)
Gareth Oliver, Peter Sunehag, Tom Gedeon
Online feature selection for Brain Computer Interfaces. CCMB (2013)
Hadi Mohasel Afshar, Peter Sunehag
On Nicod's Condition, Rules of Induction and the Raven Paradox. CoRR (2013)
Peter Sunehag, Marcus Hutter
Learning Agents with Evolving Hypothesis Classes. AGI (2013)
Phuong Minh Nguyen, Peter Sunehag, Marcus Hutter
Context Tree Maximizing. AAAI (2012)
Gareth Oliver, Peter Sunehag, Tom Gedeon
Asynchronous Brain Computer Interface using Hidden Semi-Markov Models. EMBC (2012)
Alexander O'Neill, Marcus Hutter, Wen Shao, Peter Sunehag
Adaptive Context Tree Weighting. DCC (2012)
Alexander O'Neill, Marcus Hutter, Wen Shao, Peter Sunehag
Adaptive Context Tree Weighting CoRR (2012)
Peter Sunehag, Marcus Hutter
Optimistic Agents Are Asymptotically Optimal. Australasian Conference on Artificial Intelligence (2012)
Peter Sunehag, Marcus Hutter
Optimistic AIXI. AGI (2012)
Peter Sunehag, Wen Shao, Marcus Hutter
Coding of Non-Stationary Sources as a Foundation for Detecting Change Points and Outliers in Binary Time-Series. AusDM (2012)
Gareth Oliver, Peter Sunehag, Tom Gedeon
Recursive channel selection techniques for brain computer interfaces. EMBC (2012)
Peter Sunehag, Marcus Hutter
Optimistic Agents are Asymptotically Optimal CoRR (2012)
Mayank Daswani, Peter Sunehag, Marcus Hutter
Feature Reinforcement Learning using Looping Suffix Trees. EWRL (2012)
Joel Veness, Peter Sunehag, Marcus Hutter
On Ensemble Techniques for AIXI Approximation. AGI (2012)
Phuong Minh Nguyen, Peter Sunehag, Marcus Hutter
Feature Reinforcement Learning In Practice CoRR (2011)
Matthew W. Robards, Peter Sunehag
Gradient Based Algorithms with Loss Functions and Kernels for Improved On-Policy Control. EWRL (2011)
Matthew W. Robards, Peter Sunehag, Scott Sanner, Bhaskara Marthi
Sparse Kernel-SARSA(λ) with an Eligibility Trace. ECML/PKDD (3) (2011)
Phuong Minh Nguyen, Peter Sunehag, Marcus Hutter
Feature Reinforcement Learning in Practice. EWRL (2011)
Ian Wood, Peter Sunehag, Marcus Hutter
(Non-)Equivalence of Universal Priors. Algorithmic Probability and Friends (2011)
Peter Sunehag, Marcus Hutter
Principles of Solomonoff Induction and AIXI. Algorithmic Probability and Friends (2011)
Peter Sunehag, Marcus Hutter
Axioms for Rational Reinforcement Learning CoRR (2011)
Peter Sunehag, Marcus Hutter
Axioms for Rational Reinforcement Learning. ALT (2011)
Peter Sunehag, Marcus Hutter
Principles of Solomonoff Induction and AIXI CoRR (2011)
Ian Wood, Peter Sunehag, Marcus Hutter
(Non-)Equivalence of Universal Priors CoRR (2011)
Peter Sunehag, Marcus Hutter
Consistency of Feature Markov Processes. ALT (2010)
Peter Sunehag, Marcus Hutter
Consistency of Feature Markov Processes CoRR (2010)
Owen Thomas, Peter Sunehag, Gideon Dror, Sungrack Yun, Sungwoong Kim, Matthew W. Robards, Alexander J. Smola, Daniel Green, Philo Saunders
Wearable sensor activity analysis using semi-Markov models with a grammar. Pervasive Mob. Comput. 6 (3) (2010)
Matthew W. Robards, Peter Sunehag
Semi-Markov kMeans Clustering and Activity Recognition from Body-Worn Sensors. ICDM (2009)
Peter Sunehag, Jochen Trumpf, S. V. N. Vishwanathan, Nicol N. Schraudolph
Variable Metric Stochastic Approximation Theory. AISTATS (2009)
Peter Sunehag
Emerge and spread models and word burstiness. AISTATS (2007)
Peter Sunehag
Subcouples of codimension one and interpolation of operators that almost agree. J. Approx. Theory 130 (1) (2004)