Obserwuj
Wendelin Böhmer
Tytuł
Cytowane przez
Cytowane przez
Rok
Deep coordination graphs
W Böhmer, V Kurin, S Whiteson
International Conference on Machine Learning, 980-991, 2020
1062020
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
732015
Multi-agent common knowledge reinforcement learning
C Schroeder de Witt, J Foerster, G Farquhar, P Torr, W Boehmer, ...
Advances in Neural Information Processing Systems 32, 2019
632019
Transient non-stationarity and generalisation in deep reinforcement learning
M Igl, G Farquhar, J Luketina, W Boehmer, S Whiteson
arXiv preprint arXiv:2006.05826, 2020
45*2020
Facmac: Factored multi-agent centralised policy gradients
B Peng, T Rashid, C Schroeder de Witt, PA Kamienny, P Torr, W Böhmer, ...
Advances in Neural Information Processing Systems 34, 12208-12221, 2021
412021
Generalized off-policy actor-critic
S Zhang, W Boehmer, S Whiteson
Advances in Neural Information Processing Systems 32, 2019
392019
The effect of novelty on reinforcement learning
A Houillon, RC Lorenz, W Böhmer, MA Rapp, A Heinz, J Gallinat, ...
Progress in brain research 202, 415-439, 2013
392013
Deep Multi-Agent Reinforcement Learning for Decentralized Continuous Cooperative Control
C Schroeder de Witt, B Peng, PA Kamienny, P Torr, W Böhmer, ...
arXiv e-prints, arXiv: 2003.06709, 2020
36*2020
Optimistic exploration even with a pessimistic initialisation
T Rashid, B Peng, W Boehmer, S Whiteson
arXiv preprint arXiv:2002.12174, 2020
312020
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
312014
Randomized Entity-wise Factorization for Multi-Agent Reinforcement Learning
S Iqbal, CAS De Witt, B Peng, W Böhmer, S Whiteson, F Sha
International Conference on Machine Learning, 4596-4606, 2021
30*2021
Construction of Approximation Spaces for Reinforcement Learning.
W Böhmer, S Grünewälder, Y Shen, M Musial, K Obermayer
Journal of Machine Learning Research 14 (7), 2013
282013
Multi-agent common knowledge reinforcement learning
CA Schroeder, J Foerster, G Farquhar, P Torr, W Boehmer, S Whiteson
25*2019
My body is a cage: the role of morphology in graph-based incompatible control
V Kurin, M Igl, T Rocktäschel, W Boehmer, S Whiteson
arXiv preprint arXiv:2010.01856, 2020
222020
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), 67-86, 2012
222012
Exploration with unreliable intrinsic reward in multi-agent reinforcement learning
W Böhmer, T Rashid, S Whiteson
arXiv preprint arXiv:1906.02138, 2019
212019
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
212011
Uneven: Universal value exploration for multi-agent reinforcement learning
T Gupta, A Mahajan, B Peng, W Böhmer, S Whiteson
International Conference on Machine Learning, 3930-3941, 2021
202021
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
182020
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
152019
Nie można teraz wykonać tej operacji. Spróbuj ponownie później.
Prace 1–20