Wendelin Böhmer
Title
Cited by
Cited by
Year
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
402015
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
242019
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
242013
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
212013
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
192014
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
192012
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
162011
Generalized off-policy actor-critic
S Zhang, W Boehmer, S Whiteson
Advances in Neural Information Processing Systems, 2001-2011, 2019
112019
Autonomous learning of state representations for control
W Böhmer, JT Springenberg, J Boedecker, M Riedmiller, K Obermayer
KI-Künstliche Intelligenz, 1-10, 2015
112015
Deep coordination graphs
W Böhmer, V Kurin, S Whiteson
arXiv preprint arXiv:1910.00091, 2019
72019
Multitask soft option learning
M Igl, A Gambardella, J He, N Nardelli, N Siddharth, W Böhmer, ...
arXiv preprint arXiv:1904.01033, 2019
52019
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
52016
Towards structural generalization: Factored approximate planning
W Böhmer, K Obermayer
ICRA Workshop on Autonomous Learning, 2013
52013
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
42019
Regression with linear factored functions
W Böhmer, K Obermayer
Joint European Conference on Machine Learning and Knowledge Discovery in …, 2015
42015
Optimistic exploration even with a pessimistic initialisation
T Rashid, B Peng, W Böhmer, S Whiteson
arXiv preprint arXiv:2002.12174, 2020
32020
Exploration with unreliable intrinsic reward in multi-agent reinforcement learning
W Böhmer, T Rashid, S Whiteson
arXiv preprint arXiv:1906.02138, 2019
32019
Non-deterministic policy improvement stabilizes approximated reinforcement learning
W Böhmer, R Guo, K Obermayer
arXiv preprint arXiv:1612.07548, 2016
22016
Robot Navigation using Reinforcement Learning and Slow Feature Analysis
W Böhmer
arXiv preprint arXiv:1205.0986, 2012
22012
The Impact of Non-stationarity on Generalisation in Deep Reinforcement Learning
M Igl, G Farquhar, J Luketina, W Boehmer, S Whiteson
arXiv preprint arXiv:2006.05826, 2020
12020
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Articles 1–20