| Value-decomposition networks for cooperative multi-agent learning P Sunehag, G Lever, A Gruslys, WM Czarnecki, V Zambaldi, M Jaderberg, ... arXiv preprint arXiv:1706.05296, 2017 | 408 | 2017 |
| Deep reinforcement learning in large discrete action spaces G Dulac-Arnold, R Evans, H van Hasselt, P Sunehag, T Lillicrap, J Hunt, ... arXiv preprint arXiv:1512.07679, 2015 | 274 | 2015 |
| Reinforcement learning in large discrete action spaces G Dulac-Arnold, R Evans, P Sunehag, B Coppin | 56 | 2015 |
| The sample-complexity of general reinforcement learning T Lattimore, M Hutter, P Sunehag International Conference on Machine Learning, 28-36, 2013 | 43 | 2013 |
| Wearable sensor activity analysis using semi-Markov models with a grammar O Thomas, P Sunehag, G Dror, S Yun, S Kim, M Robards, A Smola, ... Pervasive and Mobile Computing 6 (3), 342-350, 2010 | 41 | 2010 |
| Variable metric stochastic approximation theory P Sunehag, J Trumpf, SVN Vishwanathan, N Schraudolph Artificial Intelligence and Statistics, 560-566, 2009 | 39 | 2009 |
| Deep reinforcement learning with attention for slate markov decision processes with high-dimensional states and actions P Sunehag, R Evans, G Dulac-Arnold, Y Zwols, D Visentin, B Coppin arXiv preprint arXiv:1512.01124, 2015 | 25 | 2015 |
| Malthusian reinforcement learning JZ Leibo, J Perolat, E Hughes, S Wheelwright, AH Marblestone, ... arXiv preprint arXiv:1812.07019, 2018 | 20 | 2018 |
| Semi-markov kmeans clustering and activity recognition from body-worn sensors MW Robards, P Sunehag 2009 Ninth IEEE International Conference on Data Mining, 438-446, 2009 | 17 | 2009 |
| Consistency of feature Markov processes P Sunehag, M Hutter Algorithmic Learning Theory, 360-374, 2010 | 15 | 2010 |
| Rationality, optimism and guarantees in general reinforcement learning P Sunehag, M Hutter The Journal of Machine Learning Research 16 (1), 1345-1390, 2015 | 14 | 2015 |
| Adaptive context tree weighting A O'Neill, M Hutter, W Shao, P Sunehag 2012 Data Compression Conference, 317-326, 2012 | 14 | 2012 |
| Feature reinforcement learning: state of the art M Daswani, P Sunehag, M Hutter Workshops at the Twenty-Eighth AAAI Conference on Artificial Intelligence, 2014 | 13 | 2014 |
| Optimistic agents are asymptotically optimal P Sunehag, M Hutter Australasian Joint Conference on Artificial Intelligence, 15-26, 2012 | 13 | 2012 |
| Context tree maximizing PM Nguyen, P Sunehag, M Hutter Twenty-Sixth AAAI Conference on Artificial Intelligence, 2012 | 13 | 2012 |
| Feature Reinforcement Learning In Practice P Nguyen, P Sunehag, M Hutter Arxiv preprint arXiv:1108.3614, 2011 | 13 | 2011 |
| (Non-) equivalence of universal priors I Wood, P Sunehag, M Hutter Algorithmic Probability and Friends. Bayesian Prediction and Artificial …, 2013 | 12 | 2013 |
| Axioms for rational reinforcement learning P Sunehag, M Hutter Algorithmic Learning Theory, 338-352, 2011 | 10 | 2011 |
| Real method of interpolation on subcouples of codimension one SV Astashkin, P Sunehag Stud. Math., 2008 | 10 | 2008 |
| Using two-stage conditional word frequency models to model word burstiness and motivating TF-IDF P Sunehag | 10 | 2007 |
Marcus HutterResearcher@DeepMind & Professor at ANUVerified email at anu.edu.au
