Segui
Maximilian Igl
Maximilian Igl
Waymo Research
Email verificata su eng.ox.ac.uk - Home page
Titolo
Citata da
Citata da
Anno
Deep variational reinforcement learning for POMDPs
M Igl, L Zintgraf, TA Le, F Wood, S Whiteson
International Conference on Machine Learning, 2117-2126, 2018
2182018
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
1782018
Auto-encoding sequential monte carlo
TA Le, M Igl, T Rainforth, T Jin, F Wood
arXiv preprint arXiv:1705.10306, 2017
1492017
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
1392019
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
1222017
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
1082019
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
322020
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
242020
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
222021
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
202020
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
192020
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
92019
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, ...
The Journal of Machine Learning Research 22 (1), 13198-13236, 2021
82021
Symphony: Learning realistic and diverse agents for autonomous driving simulation
M Igl, D Kim, A Kuefler, P Mougin, P Shah, K Shiarlis, D Anguelov, ...
2022 International Conference on Robotics and Automation (ICRA), 2445-2451, 2022
62022
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
42021
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
Particle-Based Score Estimation for State Space Model Learning in Autonomous Driving
A Singh, O Makhlouf, M Igl, J Messias, A Doucet, S Whiteson
arXiv preprint arXiv:2212.06968, 2022
2022
Learning Skills Diverse in Value-Relevant Features
MJA Smith, J Luketina, K Hartikainen, M Igl, S Whiteson
Conference on Lifelong Learning Agents, 1174-1194, 2022
2022
Wolfson Building, Parks Road, OX1 3QD Oxford (UK) Sebastian Schulze sebastian. schulze@ eng. ox. ac. uk University of Oxford Cong Lu cong. lu@ stats. ox. ac. uk University of …
L Feng, M Igl, K Shiarlis, Y Gal, K Hofmann, S Whiteson
Journal of Machine Learning Research 22, 1-39, 2021
2021
Inductive biases and generalisation for deep reinforcement learning
M Igl
University of Oxford, 2021
2021
Il sistema al momento non può eseguire l'operazione. Riprova più tardi.
Articoli 1–20