| Autonomous learning of state representations for control: An emerging field aims to autonomously learn state representations for reinforcement learning agents from their real … W Böhmer, JT Springenberg, J Boedecker, M Riedmiller, K Obermayer KI-Künstliche Intelligenz 29 (4), 353-362, 2015 | 43 | 2015 |
| Multi-agent common knowledge reinforcement learning CS de Witt, J Foerster, G Farquhar, P Torr, W Boehmer, S Whiteson Advances in Neural Information Processing Systems, 9927-9939, 2019 | 31 | 2019 |
| The effect of novelty on reinforcement learning A Houillon, RC Lorenz, W Boehmer, MA Rapp, A Heinz, J Gallinat, ... Progress in brain research 202, 415-439, 2013 | 26 | 2013 |
| Construction of approximation spaces for reinforcement learning W Böhmer, S Grünewälder, Y Shen, M Musial, K Obermayer The Journal of Machine Learning Research 14 (1), 2067-2118, 2013 | 24 | 2013 |
| Neural systems for choice and valuation with counterfactual learning signals MJ Tobia, R Guo, U Schwarze, W Böhmer, J Gläscher, B Finckh, ... Neuroimage 89, 57-69, 2014 | 22 | 2014 |
| Generating feature spaces for linear algorithms with regularized sparse kernel slow feature analysis W Böhmer, S Grünewälder, H Nickisch, K Obermayer Machine Learning 89 (1-2), 67-86, 2012 | 20 | 2012 |
| Regularized sparse kernel slow feature analysis W Böhmer, S Grünewälder, H Nickisch, K Obermayer Joint European Conference on Machine Learning and Knowledge Discovery in …, 2011 | 17 | 2011 |
| Generalized off-policy actor-critic S Zhang, W Boehmer, S Whiteson Advances in Neural Information Processing Systems, 2001-2011, 2019 | 16 | 2019 |
| Deep coordination graphs W Böhmer, V Kurin, S Whiteson International Conference on Machine Learning, 980-991, 2020 | 13 | 2020 |
| Autonomous learning of state representations for control W Böhmer, JT Springenberg, J Boedecker, M Riedmiller, K Obermayer KI-Künstliche Intelligenz, 1-10, 2015 | 13 | 2015 |
| Exploration with unreliable intrinsic reward in multi-agent reinforcement learning W Böhmer, T Rashid, S Whiteson arXiv preprint arXiv:1906.02138, 2019 | 7 | 2019 |
| Interaction of instrumental and goal-directed learning modulates prediction error representations in the ventral striatum R Guo, W Böhmer, M Hebart, S Chien, T Sommer, K Obermayer, ... Journal of Neuroscience 36 (50), 12650-12660, 2016 | 6 | 2016 |
| Multitask soft option learning M Igl, A Gambardella, J He, N Nardelli, N Siddharth, W Böhmer, ... Conference on Uncertainty in Artificial Intelligence, 969-978, 2020 | 5 | 2020 |
| Deep Multi-Agent Reinforcement Learning for Decentralized Continuous Cooperative Control C Schroeder de Witt, B Peng, PA Kamienny, P Torr, W Böhmer, ... arXiv, arXiv: 2003.06709, 2020 | 5* | 2020 |
| Multi-agent hierarchical reinforcement learning with dynamic termination D Han, W Boehmer, M Wooldridge, A Rogers Pacific Rim International Conference on Artificial Intelligence, 80-92, 2019 | 5 | 2019 |
| Towards structural generalization: Factored approximate planning W Böhmer, K Obermayer ICRA Workshop on Autonomous Learning, 2013 | 5 | 2013 |
| Optimistic exploration even with a pessimistic initialisation T Rashid, B Peng, W Böhmer, S Whiteson arXiv preprint arXiv:2002.12174, 2020 | 4 | 2020 |
| Regression with linear factored functions W Böhmer, K Obermayer Joint European Conference on Machine Learning and Knowledge Discovery in …, 2015 | 4 | 2015 |
| Deep Residual Reinforcement Learning S Zhang, W Boehmer, S Whiteson arXiv preprint arXiv:1905.01072, 2019 | 3 | 2019 |
| Non-deterministic policy improvement stabilizes approximated reinforcement learning W Böhmer, R Guo, K Obermayer arXiv preprint arXiv:1612.07548, 2016 | 3 | 2016 |
Klaus ObermayerProfessor, Fakultät IV - Electrical Engineering and Computer Science, Technische Universität BerlinGeverifieerd e-mailadres voor tu-berlin.de
