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2020 – today
- 2023
[c34]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: 2827-2829- [i19]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 abs/2302.01180 (2023) - [i18]Yali Du, Joel Z. Leibo, Usman Islam, Richard Willis, Peter Sunehag:
A Review of Cooperation in Multi-agent Learning. CoRR abs/2312.05162 (2023) - 2022
- [i17]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 abs/2211.13746 (2022) - 2021
- [c33]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: 6187-6199 - [i16]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 abs/2107.06857 (2021) - 2020
- [c32]Jiachen Yang, Ang Li, Mehrdad Farajtabar, Peter Sunehag, Edward Hughes, Hongyuan Zha:
Learning to Incentivize Other Learning Agents. NeurIPS 2020 - [i15]Jiachen Yang, Ang Li, Mehrdad Farajtabar, Peter Sunehag, Edward Hughes, Hongyuan Zha:
Learning to Incentivize Other Learning Agents. CoRR abs/2006.06051 (2020)
2010 – 2019
- 2019
- [c31]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: 1099-1107 - [c30]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: 103-110 - 2018
- [c29]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: 2085-2087 - [i14]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 abs/1812.07019 (2018) - 2017
- [i13]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 abs/1706.05296 (2017) - 2015
- [j3]Peter Sunehag, Marcus Hutter:
Rationality, optimism and guarantees in general reinforcement learning. J. Mach. Learn. Res. 16: 1345-1390 (2015) - [c28]Peter Sunehag, Marcus Hutter:
Using Localization and Factorization to Reduce the Complexity of Reinforcement Learning. AGI 2015: 177-186 - [i12]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 abs/1512.01124 (2015) - [i11]Gabriel Dulac-Arnold, Richard Evans, Peter Sunehag, Ben Coppin:
Reinforcement Learning in Large Discrete Action Spaces. CoRR abs/1512.07679 (2015) - 2014
- [c27]Mayank Daswani, Peter Sunehag, Marcus Hutter:
Reinforcement learning with value advice. ACML 2014 - [c26]Peter Sunehag, Marcus Hutter:
Intelligence as Inference or Forcing Occam on the World. AGI 2014: 186-195 - [c25]Peter Sunehag, Marcus Hutter:
A Dual Process Theory of Optimistic Cognition. CogSci 2014 - 2013
- [c24]Mayank Daswani, Peter Sunehag, Marcus Hutter:
Q-learning for history-based reinforcement learning. ACML 2013: 213-228 - [c23]Peter Sunehag, Marcus Hutter:
Learning Agents with Evolving Hypothesis Classes. AGI 2013: 150-159 - [c22]Tor Lattimore, Marcus Hutter, Peter Sunehag:
Concentration and Confidence for Discrete Bayesian Sequence Predictors. ALT 2013: 324-338 - [c21]Gareth Oliver, Peter Sunehag, Tom Gedeon:
Online feature selection for Brain Computer Interfaces. CCMB 2013: 122-129 - [c20]Tor Lattimore, Marcus Hutter, Peter Sunehag:
The Sample-Complexity of General Reinforcement Learning. ICML (3) 2013: 28-36 - [i10]Tor Lattimore, Marcus Hutter, Peter Sunehag:
Concentration and Confidence for Discrete Bayesian Sequence Predictors. CoRR abs/1307.0127 (2013) - [i9]Hadi Mohasel Afshar, Peter Sunehag:
On Nicod's Condition, Rules of Induction and the Raven Paradox. CoRR abs/1307.3435 (2013) - [i8]Tor Lattimore, Marcus Hutter, Peter Sunehag:
The Sample-Complexity of General Reinforcement Learning. CoRR abs/1308.4828 (2013) - 2012
- [c19]Phuong Minh Nguyen, Peter Sunehag, Marcus Hutter:
Context Tree Maximizing. AAAI 2012: 1075-1082 - [c18]Peter Sunehag, Marcus Hutter:
Optimistic AIXI. AGI 2012: 312-321 - [c17]Joel Veness, Peter Sunehag, Marcus Hutter:
On Ensemble Techniques for AIXI Approximation. AGI 2012: 341-351 - [c16]Peter Sunehag, Marcus Hutter:
Optimistic Agents Are Asymptotically Optimal. Australasian Conference on Artificial Intelligence 2012: 15-26 - [c15]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: 79-84 - [c14]Alexander O'Neill, Marcus Hutter, Wen Shao, Peter Sunehag:
Adaptive Context Tree Weighting. DCC 2012: 317-326 - [c13]Gareth Oliver, Peter Sunehag, Tom Gedeon:
Recursive channel selection techniques for brain computer interfaces. EMBC 2012: 1753-1756 - [c12]Gareth Oliver, Peter Sunehag, Tom Gedeon:
Asynchronous Brain Computer Interface using Hidden Semi-Markov Models. EMBC 2012: 2728-2731 - [c11]Mayank Daswani, Peter Sunehag, Marcus Hutter:
Feature Reinforcement Learning using Looping Suffix Trees. EWRL 2012: 11-24 - [i7]Alexander O'Neill, Marcus Hutter, Wen Shao, Peter Sunehag:
Adaptive Context Tree Weighting. CoRR abs/1201.2056 (2012) - [i6]Peter Sunehag, Marcus Hutter:
Optimistic Agents are Asymptotically Optimal. CoRR abs/1210.0077 (2012) - 2011
- [c10]Peter Sunehag, Marcus Hutter:
Axioms for Rational Reinforcement Learning. ALT 2011: 338-352 - [c9]Peter Sunehag, Marcus Hutter:
Principles of Solomonoff Induction and AIXI. Algorithmic Probability and Friends 2011: 386-398 - [c8]Ian Wood, Peter Sunehag, Marcus Hutter:
(Non-)Equivalence of Universal Priors. Algorithmic Probability and Friends 2011: 417-425 - [c7]Matthew W. Robards, Peter Sunehag:
Gradient Based Algorithms with Loss Functions and Kernels for Improved On-Policy Control. EWRL 2011: 30-41 - [c6]Phuong Minh Nguyen, Peter Sunehag, Marcus Hutter:
Feature Reinforcement Learning in Practice. EWRL 2011: 66-77 - [c5]Matthew W. Robards, Peter Sunehag, Scott Sanner, Bhaskara Marthi:
Sparse Kernel-SARSA(λ) with an Eligibility Trace. ECML/PKDD (3) 2011: 1-17 - [i5]Peter Sunehag, Marcus Hutter:
Axioms for Rational Reinforcement Learning. CoRR abs/1107.5520 (2011) - [i4]Phuong Minh Nguyen, Peter Sunehag, Marcus Hutter:
Feature Reinforcement Learning In Practice. CoRR abs/1108.3614 (2011) - [i3]Ian Wood, Peter Sunehag, Marcus Hutter:
(Non-)Equivalence of Universal Priors. CoRR abs/1111.3854 (2011) - [i2]Peter Sunehag, Marcus Hutter:
Principles of Solomonoff Induction and AIXI. CoRR abs/1111.6117 (2011) - 2010
- [j2]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): 342-350 (2010) - [c4]Peter Sunehag, Marcus Hutter:
Consistency of Feature Markov Processes. ALT 2010: 360-374 - [i1]Peter Sunehag, Marcus Hutter:
Consistency of Feature Markov Processes. CoRR abs/1007.2075 (2010)
2000 – 2009
- 2009
- [c3]Matthew W. Robards, Peter Sunehag:
Semi-Markov kMeans Clustering and Activity Recognition from Body-Worn Sensors. ICDM 2009: 438-446 - [c2]Peter Sunehag, Jochen Trumpf, S. V. N. Vishwanathan, Nicol N. Schraudolph:
Variable Metric Stochastic Approximation Theory. AISTATS 2009: 560-566 - 2007
- [c1]Peter Sunehag:
Emerge and spread models and word burstiness. AISTATS 2007: 540-547 - 2004
- [j1]Peter Sunehag:
Subcouples of codimension one and interpolation of operators that almost agree. J. Approx. Theory 130(1): 78-98 (2004)
Coauthor Index
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last updated on 2024-05-02 21:48 CEST by the dblp team
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