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Maximilian Igl
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2020 – today
- 2024
- [i15]Zhiyu Huang, Xinshuo Weng, Maximilian Igl, Yuxiao Chen, Yulong Cao, Boris Ivanovic, Marco Pavone, Chen Lv:
Gen-Drive: Enhancing Diffusion Generative Driving Policies with Reward Modeling and Reinforcement Learning Fine-tuning. CoRR abs/2410.05582 (2024) - 2023
- [c16]Maximilian Igl, Punit Shah, Paul Mougin, Sirish Srinivasan, Tarun Gupta, Brandyn White, Kyriacos Shiarlis, Shimon Whiteson:
Hierarchical Imitation Learning for Stochastic Environments. IROS 2023: 1697-1704 - [i14]Maximilian Igl, Punit Shah, Paul Mougin, Sirish Srinivasan, Tarun Gupta, Brandyn White, Kyriacos Shiarlis, Shimon Whiteson:
Hierarchical Imitation Learning for Stochastic Environments. CoRR abs/2309.14003 (2023) - 2022
- [c15]Matthew J. A. Smith, Jelena Luketina, Kristian Hartikainen, Maximilian Igl, Shimon Whiteson:
Learning Skills Diverse in Value-Relevant Features. CoLLAs 2022: 1174-1194 - [c14]Angad Singh, Omar Makhlouf, Maximilian Igl, João V. Messias, Arnaud Doucet, Shimon Whiteson:
Particle-Based Score Estimation for State Space Model Learning in Autonomous Driving. CoRL 2022: 1168-1177 - [c13]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:
Communicating via Markov Decision Processes. ICML 2022: 20314-20328 - [c12]Maximilian Igl, Daewoo Kim, Alex Kuefler, Paul Mougin, Punit Shah, Kyriacos Shiarlis, Dragomir Anguelov, Mark Palatucci, Brandyn White, Shimon Whiteson:
Symphony: Learning Realistic and Diverse Agents for Autonomous Driving Simulation. ICRA 2022: 2445-2451 - [i13]Maximilian Igl, Daewoo Kim, Alex Kuefler, Paul Mougin, Punit Shah, Kyriacos Shiarlis, Dragomir Anguelov, Mark Palatucci, Brandyn White, Shimon Whiteson:
Symphony: Learning Realistic and Diverse Agents for Autonomous Driving Simulation. CoRR abs/2205.03195 (2022) - [i12]Angad Singh, Omar Makhlouf, Maximilian Igl, João V. Messias, Arnaud Doucet, Shimon Whiteson:
Particle-Based Score Estimation for State Space Model Learning in Autonomous Driving. CoRR abs/2212.06968 (2022) - 2021
- [b1]Maximilian Igl:
Inductive biases and generalisation for deep reinforcement learning. University of Oxford, UK, 2021 - [j1]Luisa M. Zintgraf, Sebastian Schulze, Cong Lu, Leo Feng, Maximilian Igl, Kyriacos Shiarlis, Yarin Gal, Katja Hofmann, Shimon Whiteson:
VariBAD: Variational Bayes-Adaptive Deep RL via Meta-Learning. J. Mach. Learn. Res. 22: 289:1-289:39 (2021) - [c11]Maximilian Igl, Gregory Farquhar, Jelena Luketina, Wendelin Boehmer, Shimon Whiteson:
Transient Non-stationarity and Generalisation in Deep Reinforcement Learning. ICLR 2021 - [c10]Vitaly Kurin, Maximilian Igl, Tim Rocktäschel, Wendelin Boehmer, Shimon Whiteson:
My Body is a Cage: the Role of Morphology in Graph-Based Incompatible Control. ICLR 2021 - [c9]Luisa M. Zintgraf, Leo Feng, Cong Lu, Maximilian Igl, Kristian Hartikainen, Katja Hofmann, Shimon Whiteson:
Exploration in Approximate Hyper-State Space for Meta Reinforcement Learning. ICML 2021: 12991-13001 - [c8]Charlie Blake, Vitaly Kurin, Maximilian Igl, Shimon Whiteson:
Snowflake: Scaling GNNs to high-dimensional continuous control via parameter freezing. NeurIPS 2021: 23983-23992 - [i11]Charlie Blake, Vitaly Kurin, Maximilian Igl, Shimon Whiteson:
Snowflake: Scaling GNNs to High-Dimensional Continuous Control via Parameter Freezing. CoRR abs/2103.01009 (2021) - [i10]Samuel Sokota, Christian Schröder de Witt, Maximilian Igl, Luisa M. Zintgraf, Philip H. S. Torr, Shimon Whiteson, Jakob N. Foerster:
Implicit Communication as Minimum Entropy Coupling. CoRR abs/2107.08295 (2021) - 2020
- [c7]Luisa M. Zintgraf, Kyriacos Shiarlis, Maximilian Igl, Sebastian Schulze, Yarin Gal, Katja Hofmann, Shimon Whiteson:
VariBAD: A Very Good Method for Bayes-Adaptive Deep RL via Meta-Learning. ICLR 2020 - [c6]Maximilian Igl, Andrew Gambardella, Jinke He, Nantas Nardelli, N. Siddharth, Wendelin Boehmer, Shimon Whiteson:
Multitask Soft Option Learning. UAI 2020: 969-978 - [i9]Maximilian Igl, Gregory Farquhar, Jelena Luketina, Wendelin Boehmer, Shimon Whiteson:
The Impact of Non-stationarity on Generalisation in Deep Reinforcement Learning. CoRR abs/2006.05826 (2020) - [i8]Luisa M. Zintgraf, Leo Feng, Maximilian Igl, Kristian Hartikainen, Katja Hofmann, Shimon Whiteson:
Exploration in Approximate Hyper-State Space for Meta Reinforcement Learning. CoRR abs/2010.01062 (2020) - [i7]Vitaly Kurin, Maximilian Igl, Tim Rocktäschel, Wendelin Boehmer, Shimon Whiteson:
My Body is a Cage: the Role of Morphology in Graph-Based Incompatible Control. CoRR abs/2010.01856 (2020)
2010 – 2019
- 2019
- [c5]Maximilian Igl, Kamil Ciosek, Yingzhen Li, Sebastian Tschiatschek, Cheng Zhang, Sam Devlin, Katja Hofmann:
Generalization in Reinforcement Learning with Selective Noise Injection and Information Bottleneck. NeurIPS 2019: 13956-13968 - [i6]Maximilian Igl, Andrew Gambardella, Nantas Nardelli, N. Siddharth, Wendelin Böhmer, Shimon Whiteson:
Multitask Soft Option Learning. CoRR abs/1904.01033 (2019) - [i5]Luisa M. Zintgraf, Kyriacos Shiarlis, Maximilian Igl, Sebastian Schulze, Yarin Gal, Katja Hofmann, Shimon Whiteson:
VariBAD: A Very Good Method for Bayes-Adaptive Deep RL via Meta-Learning. CoRR abs/1910.08348 (2019) - [i4]Maximilian Igl, Kamil Ciosek, Yingzhen Li, Sebastian Tschiatschek, Cheng Zhang, Sam Devlin, Katja Hofmann:
Generalization in Reinforcement Learning with Selective Noise Injection and Information Bottleneck. CoRR abs/1910.12911 (2019) - 2018
- [c4]Gregory Farquhar, Tim Rocktäschel, Maximilian Igl, Shimon Whiteson:
TreeQN and ATreeC: Differentiable Tree-Structured Models for Deep Reinforcement Learning. ICLR (Poster) 2018 - [c3]Tuan Anh Le, Maximilian Igl, Tom Rainforth, Tom Jin, Frank Wood:
Auto-Encoding Sequential Monte Carlo. ICLR (Poster) 2018 - [c2]Maximilian Igl, Luisa M. Zintgraf, Tuan Anh Le, Frank Wood, Shimon Whiteson:
Deep Variational Reinforcement Learning for POMDPs. ICML 2018: 2122-2131 - [c1]Tom Rainforth, Adam R. Kosiorek, Tuan Anh Le, Chris J. Maddison, Maximilian Igl, Frank Wood, Yee Whye Teh:
Tighter Variational Bounds are Not Necessarily Better. ICML 2018: 4274-4282 - [i3]Tom Rainforth, Adam R. Kosiorek, Tuan Anh Le, Chris J. Maddison, Maximilian Igl, Frank Wood, Yee Whye Teh:
Tighter Variational Bounds are Not Necessarily Better. CoRR abs/1802.04537 (2018) - [i2]Maximilian Igl, Luisa M. Zintgraf, Tuan Anh Le, Frank Wood, Shimon Whiteson:
Deep Variational Reinforcement Learning for POMDPs. CoRR abs/1806.02426 (2018) - 2017
- [i1]Gregory Farquhar, Tim Rocktäschel, Maximilian Igl, Shimon Whiteson:
TreeQN and ATreeC: Differentiable Tree Planning for Deep Reinforcement Learning. CoRR abs/1710.11417 (2017)
Coauthor Index
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last updated on 2024-11-19 20:48 CET by the dblp team
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