no code implementations • 8 Aug 2023 • Jakub Łyskawa, Paweł Wawrzyński
Reinforcement learning (RL) methods work in discrete time.
1 code implementation • 31 Jul 2023 • Bartłomiej Olber, Krystian Radlak, Krystian Chachuła, Jakub Łyskawa, Piotr Frątczak
Object detection is essential to many perception algorithms used in modern robotics applications.
1 code implementation • 25 Nov 2022 • Krystian Chachuła, Jakub Łyskawa, Bartłomiej Olber, Piotr Frątczak, Adam Popowicz, Krystian Radlak
This approach shows promising potential in rectifying state-of-the-art object detection datasets.
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 • 8 Apr 2021 • Jakub Łyskawa, Paweł Wawrzyński
It is not feasible because it causes the controlled system to jerk, and does not ensure sufficient exploration since a~single action is not long enough to create a~significant experience that could be translated into policy improvement.
1 code implementation • 10 Sep 2020 • Marcin Szulc, Jakub Łyskawa, Paweł Wawrzyński
Consequently, an agent learns from experiments that are distributed over time and potentially give better clues to policy improvement.