no code implementations • 1 Mar 2024 • Michal Nauman, Michał Bortkiewicz, Mateusz Ostaszewski, Piotr Miłoś, Tomasz Trzciński, Marek Cygan
We tested these agents across 14 diverse tasks from 2 simulation benchmarks.
no code implementations • 5 Feb 2024 • Maciej Wołczyk, Bartłomiej Cupiał, Mateusz Ostaszewski, Michał Bortkiewicz, Michał Zając, Razvan Pascanu, Łukasz Kuciński, Piotr Miłoś
Fine-tuning is a widespread technique that allows practitioners to transfer pre-trained capabilities, as recently showcased by the successful applications of foundation models.
2 code implementations • 29 Nov 2022 • Samuel Kessler, Mateusz Ostaszewski, Michał Bortkiewicz, Mateusz Żarski, Maciej Wołczyk, Jack Parker-Holder, Stephen J. Roberts, Piotr Miłoś
World models power some of the most efficient reinforcement learning algorithms.
no code implementations • 11 Nov 2022 • Michał Bortkiewicz, Jakub Łyskawa, Paweł Wawrzyński, Mateusz Ostaszewski, Artur Grudkowski, Tomasz Trzciński
In this paper, we address this gap in the state-of-the-art approaches and propose a method in which the validity of higher-level actions (thus lower-level goals) is constantly verified at the higher level.
Hierarchical Reinforcement Learning reinforcement-learning +1
no code implementations • 4 Jul 2022 • Stanisław Pawlak, Filip Szatkowski, Michał Bortkiewicz, Jan Dubiński, Tomasz Trzciński
We introduce a new method for internal replay that modulates the frequency of rehearsal based on the depth of the network.