Maximilian Igl

According to our database, Maximilian Igl authored at least 24 papers between 2017 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2024

Gen-Drive: Enhancing Diffusion Generative Driving Policies with Reward Modeling and Reinforcement Learning Fine-tuning.

Zhiyu Huang, Xinshuo Weng, Maximilian Igl, Yuxiao Chen, Yulong Cao, Boris Ivanovic, Marco Pavone, Chen Lv

CoRR, 2024

2023

Hierarchical Imitation Learning for Stochastic Environments.

Maximilian Igl, Punit Shah, Paul Mougin, Sirish Srinivasan, Tarun Gupta, Brandyn White, Kyriacos Shiarlis, Shimon Whiteson

IROS, 2023

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

Proceedings of the 2022 International Conference on Robotics and Automation, 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

Proceedings of the International Conference on Machine Learning, 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

Proceedings of the Conference on Robot Learning, 2022

Learning Skills Diverse in Value-Relevant Features.

Matthew J. A. Smith, Jelena Luketina, Kristian Hartikainen, Maximilian Igl, Shimon Whiteson

Proceedings of the Conference on Lifelong Learning Agents, 2022

2021

Inductive biases and generalisation for deep reinforcement learning

Maximilian Igl

PhD thesis, 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

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

Snowflake: Scaling GNNs to high-dimensional continuous control via parameter freezing.

Charlie Blake, Vitaly Kurin, Maximilian Igl, Shimon Whiteson

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 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

Proceedings of the 38th International Conference on Machine Learning, 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

Proceedings of the 9th International Conference on Learning Representations, 2021

Transient Non-stationarity and Generalisation in Deep Reinforcement Learning.

Maximilian Igl, Gregory Farquhar, Jelena Luketina, Wendelin Boehmer, Shimon Whiteson

Proceedings of the 9th International Conference on Learning Representations, 2021

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

The Impact of Non-stationarity on Generalisation in Deep Reinforcement Learning.

Maximilian Igl, Gregory Farquhar, Jelena Luketina, Wendelin Boehmer, Shimon Whiteson

CoRR, 2020

Multitask Soft Option Learning.

Maximilian Igl, Andrew Gambardella, Jinke He, Nantas Nardelli, N. Siddharth, Wendelin Boehmer, Shimon Whiteson

Proceedings of the Thirty-Sixth Conference on Uncertainty in Artificial Intelligence, 2020

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

Proceedings of the 8th International Conference on Learning Representations, 2020

2019

Multitask Soft Option Learning.

Maximilian Igl, Andrew Gambardella, Nantas Nardelli, N. Siddharth, Wendelin Böhmer, Shimon Whiteson

CoRR, 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

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

2018

Tighter Variational Bounds are Not Necessarily Better.

Tom Rainforth, Adam R. Kosiorek, Tuan Anh Le, Chris J. Maddison, Maximilian Igl, Frank Wood, Yee Whye Teh

Proceedings of the 35th International Conference on Machine Learning, 2018

Deep Variational Reinforcement Learning for POMDPs.

Maximilian Igl, Luisa M. Zintgraf, Tuan Anh Le, Frank Wood, Shimon Whiteson

Proceedings of the 35th International Conference on Machine Learning, 2018

Auto-Encoding Sequential Monte Carlo.

Tuan Anh Le, Maximilian Igl, Tom Rainforth, Tom Jin, Frank Wood

Proceedings of the 6th International Conference on Learning Representations, 2018

TreeQN and ATreeC: Differentiable Tree-Structured Models for Deep Reinforcement Learning.

Gregory Farquhar, Tim Rocktäschel, Maximilian Igl, Shimon Whiteson

Proceedings of the 6th International Conference on Learning Representations, 2018

2017

TreeQN and ATreeC: Differentiable Tree Planning for Deep Reinforcement Learning.

Gregory Farquhar, Tim Rocktäschel, Maximilian Igl, Shimon Whiteson

CoRR, 2017