Maximilian Igl
Title
Cited by
Cited by
Year
Tighter variational bounds are not necessarily better
T Rainforth, A Kosiorek, TA Le, C Maddison, M Igl, F Wood, YW Teh
International Conference on Machine Learning, 4277-4285, 2018
1152018
Deep variational reinforcement learning for POMDPs
M Igl, L Zintgraf, TA Le, F Wood, S Whiteson
International Conference on Machine Learning, 2117-2126, 2018
992018
Auto-encoding sequential monte carlo
TA Le, M Igl, T Rainforth, T Jin, F Wood
arXiv preprint arXiv:1705.10306, 2017
982017
Treeqn and atreec: Differentiable tree-structured models for deep reinforcement learning
G Farquhar, T Rocktäschel, M Igl, S Whiteson
arXiv preprint arXiv:1710.11417, 2017
772017
Varibad: A very good method for bayes-adaptive deep rl via meta-learning
L Zintgraf, K Shiarlis, M Igl, S Schulze, Y Gal, K Hofmann, S Whiteson
arXiv preprint arXiv:1910.08348, 2019
342019
Generalization in reinforcement learning with selective noise injection and information bottleneck
M Igl, K Ciosek, Y Li, S Tschiatschek, C Zhang, S Devlin, K Hofmann
arXiv preprint arXiv:1910.12911, 2019
212019
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
62020
Variational task embeddings for fast adapta-tion in deep reinforcement learning
L Zintgraf, M Igl, K Shiarlis, A Mahajan, K Hofmann, S Whiteson
International Conference on Learning Representations Workshop (ICLRW), 2019
52019
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
42020
Varibad: a very good method for bayes-adaptive deep rl via meta-learning
S Schulze, S Whiteson, L Zintgraf, M Igl, Y Gal, K Shiarlis, K Hofmann
International Conference on Learning Representations, 2020
32020
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
12020
Exploration in Approximate Hyper-State Space for Meta Reinforcement Learning
L Zintgraf, L Feng, M Igl, K Hartikainen, K Hofmann, S Whiteson
arXiv preprint arXiv:2010.01062, 2020
2020
Generalization in Reinforcement Learning with Selective Noise Injection and Information
M Igl, K Ciosek, Y Li, S Tschiatschek, C Zhang, S Devlin, K Hofmann
2019
Composition of queries in probabilistic programming languages
M Igl
2016
Soft Option Transfer
J He, M Igl, M Smith, W Boehmer, S Whiteson
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Articles 1–15