TreeQN and ATreeC: Differentiable Tree Planning for Deep Reinforcement Learning. - Gregory Farquhar; Tim Rocktäschel; Maximilian Igl; Shimon Whiteson (CoRR, 2017)

TreeQN and ATreeC: Differentiable Tree-Structured Models for Deep Reinforcement Learning. - Gregory Farquhar; Tim Rocktäschel; Maximilian Igl; Shimon Whiteson (ICLR (Poster), 2018)

Auto-Encoding Sequential Monte Carlo. - Tuan Anh Le 001; Maximilian Igl; Tom Rainforth; Tom Jin; Frank Wood (ICLR (Poster), 2018)

Deep Variational Reinforcement Learning for POMDPs. - Maximilian Igl; Luisa M. Zintgraf; Tuan Anh Le 001; Frank Wood; Shimon Whiteson (ICML, 2018)

Tighter Variational Bounds are Not Necessarily Better. - Tom Rainforth; Adam R. Kosiorek; Tuan Anh Le 001; Chris J. Maddison; Maximilian Igl; Frank Wood; Yee Whye Teh (ICML, 2018)

Generalization in Reinforcement Learning with Selective Noise Injection and Information Bottleneck. - Maximilian Igl; Kamil Ciosek; Yingzhen Li; Sebastian Tschiatschek; Cheng Zhang 005; Sam Devlin; Katja Hofmann (NeurIPS, 2019)

Multitask Soft Option Learning. - Maximilian Igl; Andrew Gambardella; Nantas Nardelli; N. Siddharth 001; Wendelin Böhmer; Shimon Whiteson (CoRR, 2019)

VariBAD: A Very Good Method for Bayes-Adaptive Deep RL via Meta-Learning. - Luisa M. Zintgraf; Kyriacos Shiarlis; Maximilian Igl; Sebastian Schulze; Yarin Gal; Katja Hofmann; Shimon Whiteson (ICLR, 2020)

Multitask Soft Option Learning. - Maximilian Igl; Andrew Gambardella; Jinke He; Nantas Nardelli; N. Siddharth 001; Wendelin Boehmer; Shimon Whiteson (UAI, 2020)

The Impact of Non-stationarity on Generalisation in Deep Reinforcement Learning. - Maximilian Igl; Gregory Farquhar; Jelena Luketina; Wendelin Boehmer; Shimon Whiteson (CoRR, 2020)

Exploration in Approximate Hyper-State Space for Meta Reinforcement Learning. - Luisa M. Zintgraf; Leo Feng; Maximilian Igl; Kristian Hartikainen; Katja Hofmann; Shimon Whiteson (CoRR, 2020)

Transient Non-stationarity and Generalisation in Deep Reinforcement Learning. - Maximilian Igl; Gregory Farquhar; Jelena Luketina; Wendelin Boehmer; 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 (ICLR, 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 (ICML, 2021)

Snowflake: Scaling GNNs to high-dimensional continuous control via parameter freezing. - Charlie Blake; Vitaly Kurin; Maximilian Igl; Shimon Whiteson (NeurIPS, 2021)

Implicit Communication as Minimum Entropy Coupling. - Samuel Sokota; Christian Schröder de Witt; Maximilian Igl; Luisa M. Zintgraf; Philip H. S. Torr; Shimon Whiteson; Jakob N. Foerster (CoRR, 2021)

VariBAD: Variational Bayes-Adaptive Deep RL via Meta-Learning. - Luisa M. Zintgraf; Sebastian Schulze; Cong Lu; Leo Feng; Maximilian Igl; Kyriacos Shiarlis; Yarin Gal; Katja Hofmann; Shimon Whiteson (J. Mach. Learn. Res., 2021)

Inductive biases and generalisation for deep reinforcement learning - Maximilian Igl (2021)

Learning Skills Diverse in Value-Relevant Features. - Matthew J. A. Smith; Jelena Luketina; Kristian Hartikainen; Maximilian Igl; Shimon Whiteson (CoLLAs, 2022)

Particle-Based Score Estimation for State Space Model Learning in Autonomous Driving. - Angad Singh; Omar Makhlouf; Maximilian Igl; João V. Messias; Arnaud Doucet; Shimon Whiteson (CoRL, 2022)

Communicating via Markov Decision Processes. - Samuel Sokota; Christian A. Schröder de Witt; Maximilian Igl; Luisa M. Zintgraf; Philip H. S. Torr; Martin Strohmeier; J. Zico Kolter; Shimon Whiteson; Jakob N. Foerster (ICML, 2022)

Symphony: Learning Realistic and Diverse Agents for Autonomous Driving Simulation. - Maximilian Igl; Daewoo Kim; Alex Kuefler; Paul Mougin; Punit Shah; Kyriacos Shiarlis; Dragomir Anguelov; Mark Palatucci; Brandyn White; Shimon Whiteson (ICRA, 2022)

Hierarchical Imitation Learning for Stochastic Environments. - Maximilian Igl; Punit Shah; Paul Mougin; Sirish Srinivasan; Tarun Gupta 002; Brandyn White; Kyriacos Shiarlis; Shimon Whiteson (IROS, 2023)

Gen-Drive: Enhancing Diffusion Generative Driving Policies with Reward Modeling and Reinforcement Learning Fine-tuning. - Zhiyu Huang; Xinshuo Weng; Maximilian Igl; Yuxiao Chen 008; Yulong Cao; Boris Ivanovic; Marco Pavone 001; Chen Lv (CoRR, 2024)

Closed-Loop Supervised Fine-Tuning of Tokenized Traffic Models. - Zhejun Zhang; Péter Karkus; Maximilian Igl; Wenhao Ding; Yuxiao Chen 008; Boris Ivanovic; Marco Pavone 001 (CVPR, 2025)

STORM: Spatio-TempOral Reconstruction Model For Large-Scale Outdoor Scenes. - Jiawei Yang 002; Jiahui Huang; Boris Ivanovic; Yuxiao Chen 008; Yan Wang 051; Boyi Li; Yurong You; Apoorva Sharma; Maximilian Igl; Péter Karkus; Danfei Xu; Yue Wang 041; et al. (ICLR, 2025)