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Maximilian Igl
Maximilian Igl
Waymo Research
Zweryfikowany adres z eng.ox.ac.uk - Strona główna
Tytuł
Cytowane przez
Cytowane przez
Rok
Deep variational reinforcement learning for POMDPs
M Igl, L Zintgraf, TA Le, F Wood, S Whiteson
International Conference on Machine Learning, 2117-2126, 2018
1972018
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
1682018
Auto-encoding sequential monte carlo
TA Le, M Igl, T Rainforth, T Jin, F Wood
arXiv preprint arXiv:1705.10306, 2017
1432017
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
1162017
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
1152019
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
Advances in neural information processing systems 32, 2019
892019
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
242020
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
192020
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
182020
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
162020
Exploration in approximate hyper-state space for meta reinforcement learning
LM Zintgraf, L Feng, C Lu, M Igl, K Hartikainen, K Hofmann, S Whiteson
International Conference on Machine Learning, 12991-13001, 2021
122021
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
72019
VariBAD: Variational Bayes-Adaptive Deep RL via Meta-Learning
L Zintgraf, S Schulze, C Lu, L Feng, M Igl, K Shiarlis, Y Gal, K Hofmann, ...
Journal of Machine Learning Research 22 (289), 1-39, 2021
42021
Snowflake: Scaling GNNs to high-dimensional continuous control via parameter freezing
C Blake, V Kurin, M Igl, S Whiteson
Advances in Neural Information Processing Systems 34, 23983-23992, 2021
32021
Communicating via Markov Decision Processes
S Sokota, CAS De Witt, M Igl, LM Zintgraf, P Torr, M Strohmeier, Z Kolter, ...
International Conference on Machine Learning, 20314-20328, 2022
12022
Symphony: Learning Realistic and Diverse Agents for Autonomous Driving Simulation
M Igl, D Kim, A Kuefler, P Mougin, P Shah, K Shiarlis, D Anguelov, ...
arXiv preprint arXiv:2205.03195, 2022
12022
Implicit Communication as Minimum Entropy Coupling
S Sokota, CS de Witt, M Igl, L Zintgraf, P Torr, S Whiteson, J Foerster
arXiv preprint arXiv:2107.08295, 2021
2021
Inductive biases and generalisation for deep reinforcement learning
M Igl
University of Oxford, 2021
2021
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
Inference and Distillation for Option Learning
M Igl, W Boehmer, A Gambardella, PHS Torr, N Nardelli, N Siddharth, ...
Workshop on Probabilistic Reinforcement Learning and Structured Control …, 2018
2018
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