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Michal Bortkiewicz
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Conference and Workshop Papers
- 2024
- [c5]Michal Bortkiewicz, Jakub Lyskawa, Pawel Wawrzynski, Mateusz Ostaszewski, Artur Grudkowski, Bartlomiej Sobieski, Tomasz Trzcinski:
Subgoal Reachability in Goal Conditioned Hierarchical Reinforcement Learning. ICAART (1) 2024: 221-230 - [c4]Michal Nauman, Michal Bortkiewicz, Piotr Milos, Tomasz Trzcinski, Mateusz Ostaszewski, Marek Cygan:
Overestimation, Overfitting, and Plasticity in Actor-Critic: the Bitter Lesson of Reinforcement Learning. ICML 2024 - [c3]Maciej Wolczyk, Bartlomiej Cupial, Mateusz Ostaszewski, Michal Bortkiewicz, Michal Zajac, Razvan Pascanu, Lukasz Kucinski, Piotr Milos:
Fine-tuning Reinforcement Learning Models is Secretly a Forgetting Mitigation Problem. ICML 2024 - 2023
- [c2]Samuel Kessler, Mateusz Ostaszewski, Michal Pawel Bortkiewicz, Mateusz Zarski, Maciej Wolczyk, Jack Parker-Holder, Stephen J. Roberts, Piotr Milos:
The Effectiveness of World Models for Continual Reinforcement Learning. CoLLAs 2023: 184-204 - 2022
- [c1]Stanislaw Pawlak
, Filip Szatkowski
, Michal Bortkiewicz
, Jan Dubinski
, Tomasz Trzcinski
:
Progressive Latent Replay for Efficient Generative Rehearsal. ICONIP (4) 2022: 457-467
Informal and Other Publications
- 2024
- [i4]Maciej Wolczyk, Bartlomiej Cupial, Mateusz Ostaszewski, Michal Bortkiewicz, Michal Zajac, Razvan Pascanu, Lukasz Kucinski, Piotr Milos:
Fine-tuning Reinforcement Learning Models is Secretly a Forgetting Mitigation Problem. CoRR abs/2402.02868 (2024) - [i3]Michal Nauman, Michal Bortkiewicz, Mateusz Ostaszewski, Piotr Milos, Tomasz Trzcinski, Marek Cygan:
Overestimation, Overfitting, and Plasticity in Actor-Critic: the Bitter Lesson of Reinforcement Learning. CoRR abs/2403.00514 (2024) - 2022
- [i2]Stanislaw Pawlak, Filip Szatkowski, Michal Bortkiewicz, Jan Dubinski, Tomasz Trzcinski:
Progressive Latent Replay for efficient Generative Rehearsal. CoRR abs/2207.01562 (2022) - [i1]Michal Bortkiewicz, Jakub Lyskawa, Pawel Wawrzynski, Mateusz Ostaszewski, Artur Grudkowski, Tomasz Trzcinski:
Emergency action termination for immediate reaction in hierarchical reinforcement learning. CoRR abs/2211.06351 (2022)
Coauthor Index
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