Igl, Maximilian

17 publications

CVPR 2025 Closed-Loop Supervised Fine-Tuning of Tokenized Traffic Models Zhejun Zhang, Peter Karkus, Maximilian Igl, Wenhao Ding, Yuxiao Chen, Boris Ivanovic, Marco Pavone
ICLR 2025 STORM: Spatio-TempOral Reconstruction Model for Large-Scale Outdoor Scenes Jiawei Yang, Jiahui Huang, Boris Ivanovic, Yuxiao Chen, Yan Wang, Boyi Li, Yurong You, Apoorva Sharma, Maximilian Igl, Peter Karkus, Danfei Xu, Yue Wang, Marco Pavone
ICML 2022 Communicating via Markov Decision Processes Samuel Sokota, Christian A Schroeder De Witt, Maximilian Igl, Luisa M Zintgraf, Philip Torr, Martin Strohmeier, Zico Kolter, Shimon Whiteson, Jakob Foerster
CoLLAs 2022 Learning Skills Diverse in Value-Relevant Features Matthew J. A. Smith, Jelena Luketina, Kristian Hartikainen, Maximilian Igl, Shimon Whiteson
CoRL 2022 Particle-Based Score Estimation for State Space Model Learning in Autonomous Driving Angad Singh, Omar Makhlouf, Maximilian Igl, Joao Messias, Arnaud Doucet, Shimon Whiteson
ICML 2021 Exploration in Approximate Hyper-State Space for Meta Reinforcement Learning Luisa M Zintgraf, Leo Feng, Cong Lu, Maximilian Igl, Kristian Hartikainen, Katja Hofmann, Shimon Whiteson
ICLR 2021 My Body Is a Cage: The Role of Morphology in Graph-Based Incompatible Control Vitaly Kurin, Maximilian Igl, Tim Rocktäschel, Wendelin Boehmer, Shimon Whiteson
NeurIPS 2021 Snowflake: Scaling GNNs to High-Dimensional Continuous Control via Parameter Freezing Charles Blake, Vitaly Kurin, Maximilian Igl, Shimon Whiteson
ICLR 2021 Transient Non-Stationarity and Generalisation in Deep Reinforcement Learning Maximilian Igl, Gregory Farquhar, Jelena Luketina, Wendelin Boehmer, Shimon Whiteson
JMLR 2021 VariBAD: Variational Bayes-Adaptive Deep RL via Meta-Learning Luisa Zintgraf, Sebastian Schulze, Cong Lu, Leo Feng, Maximilian Igl, Kyriacos Shiarlis, Yarin Gal, Katja Hofmann, Shimon Whiteson
UAI 2020 Multitask Soft Option Learning Maximilian Igl, Andrew Gambardella, Jinke He, Nantas Nardelli, N Siddharth, Wendelin Boehmer, Shimon Whiteson
ICLR 2020 VariBAD: A Very Good Method for Bayes-Adaptive Deep RL via Meta-Learning Luisa Zintgraf, Kyriacos Shiarlis, Maximilian Igl, Sebastian Schulze, Yarin Gal, Katja Hofmann, Shimon Whiteson
NeurIPS 2019 Generalization in Reinforcement Learning with Selective Noise Injection and Information Bottleneck Maximilian Igl, Kamil Ciosek, Yingzhen Li, Sebastian Tschiatschek, Cheng Zhang, Sam Devlin, Katja Hofmann
ICLR 2018 Auto-Encoding Sequential Monte Carlo Tuan Anh Le, Maximilian Igl, Tom Rainforth, Tom Jin, Frank Wood
ICML 2018 Deep Variational Reinforcement Learning for POMDPs Maximilian Igl, Luisa Zintgraf, Tuan Anh Le, Frank Wood, Shimon Whiteson
ICML 2018 Tighter Variational Bounds Are Not Necessarily Better Tom Rainforth, Adam Kosiorek, Tuan Anh Le, Chris Maddison, Maximilian Igl, Frank Wood, Yee Whye Teh
ICLR 2018 TreeQN and ATreeC: Differentiable Tree-Structured Models for Deep Reinforcement Learning Gregory Farquhar, Tim Rocktäschel, Maximilian Igl, Shimon Whiteson