Wendelin Böhmer
Wendelin Böhmer
Whiteson Research Lab, University of Oxford
Verifierad e-postadress på cs.ox.ac.uk - Startsida
TitelCiteras avÅr
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, 1-10, 2015
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
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
Neural systems for choice and valuation with counterfactual learning signals
MJ Tobia, R Guo, U Schwarze, W Boehmer, J Gläscher, B Finckh, ...
NeuroImage 89, 57-69, 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
Regularized sparse kernel slow feature analysis
W Böhmer, S Grünewälder, H Nickisch, K Obermayer
Machine Learning and Knowledge Discovery in Databases, 235-248, 2011
Multi-agent common knowledge reinforcement learning
JN Foerster, CAS de Witt, G Farquhar, PHS Torr, W Boehmer, S Whiteson
arXiv preprint arXiv:1810.11702, 2018
Generalized off-policy actor-critic
S Zhang, W Boehmer, S Whiteson
Advances in Neural Information Processing Systems, 1999-2009, 2019
Regression with linear factored functions
W Böhmer, K Obermayer
Joint European Conference on Machine Learning and Knowledge Discovery in …, 2015
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
Towards Structural Generalization: Factored Approximate Planning
W Böhmer, K Obermayer
ICRA Workshop on Autonomous Learning, 2013
Non-deterministic policy improvement stabilizes approximated reinforcement learning
W Böhmer, R Guo, K Obermayer
arXiv preprint arXiv:1612.07548, 2016
Exploration with unreliable intrinsic reward in multi-agent reinforcement learning
W Böhmer, T Rashid, S Whiteson
arXiv preprint arXiv:1906.02138, 2019
Robot Navigation using Reinforcement Learning and Slow Feature Analysis
W Böhmer
arXiv preprint arXiv:1205.0986, 2012
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
Reconstruct and Represent Video Contents for Captioning via Reinforcement Learning
W Zhang, B Wang, L Ma, W Liu
IEEE transactions on pattern analysis and machine intelligence, 2019
Intelligent User Association for Symbiotic Radio Networks using Deep Reinforcement Learning
Q Zhang, YC Liang, HV Poor
arXiv preprint arXiv:1905.04041, 2019
Multitask Soft Option Learning
M Igl, A Gambardella, N Nardelli, N Siddharth, W Böhmer, S Whiteson
arXiv preprint arXiv:1904.01033, 2019
Multi-agent common knowledge reinforcement learning
CA Schroeder de Witt, JN Foerster, G Farquhar, PHS Torr, W Boehmer, ...
arXiv preprint arXiv:1810.11702, 2018
Representation and generalization in autonomous reinforcement learning
W Böhmer
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Artiklar 1–20