| Deep variational reinforcement learning for POMDPs M Igl, L Zintgraf, TA Le, F Wood, S Whiteson International Conference on Machine Learning, 2117-2126, 2018 | 242 | 2018 |
| 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 | 189 | 2018 |
| 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 | 167 | 2019 |
| Auto-encoding sequential monte carlo TA Le, M Igl, T Rainforth, T Jin, F Wood arXiv preprint arXiv:1705.10306, 2017 | 160 | 2017 |
| 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 | 129 | 2017 |
| 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 | 128 | 2019 |
| 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 | 43 | 2020 |
| 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 | 34 | 2020 |
| 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 | 30 | 2021 |
| 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 | 22 | 2020 |
| 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 | 21 | 2020 |
| 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 | 15 | 2022 |
| 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 | 13 | 2021 |
| 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 | 9 | 2019 |
| 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 | 8 | 2021 |
| 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 | 4 | 2022 |
| Generalized reinforcement learning agent SM Devlin, IGL Maximilian, KA Ciosek, LI Yingzhen, S Tschiatschek, ... US Patent App. 18/052,202, 2023 | | 2023 |
| Generating simulated agent trajectories using parallel beam search KC Shiarlis, D Anguelov, BA White, SA Whiteson, IGL Maximilian, D Kim, ... US Patent App. 17/947,046, 2023 | | 2023 |
| Particle-Based Score Estimation for State Space Model Learning in Autonomous Driving A Singh, O Makhlouf, M Igl, J Messias, A Doucet, S Whiteson Conference on Robot Learning, 1168-1177, 2023 | | 2023 |
| Latent Hierarchical Imitation Learning for Stochastic Environments M Igl, P Shah, P Mougin, S Srinivasan, T Gupta, B White, K Shiarlis, ... | | 2023 |
Shimon WhitesonProfessor of Computer Science, University of Oxford / Head of Research, Waymo UKVerified email at cs.ox.ac.uk
