1 code implementation • 22 Nov 2021 • Michał Zawalski, Błażej Osiński, Henryk Michalewski, Piotr Miłoś
The key advantage of our algorithm is its high scalability in a multi-worker setting.
Multi-agent Reinforcement Learning reinforcement-learning +2
no code implementations • 28 Sep 2021 • Matt Vitelli, Yan Chang, Yawei Ye, Maciej Wołczyk, Błażej Osiński, Moritz Niendorf, Hugo Grimmett, Qiangui Huang, Ashesh Jain, Peter Ondruska
To combat this, our approach uses a simple yet effective rule-based fallback layer that performs sanity checks on an ML planner's decisions (e. g. avoiding collision, assuring physical feasibility).
no code implementations • 27 Sep 2021 • Oliver Scheel, Luca Bergamini, Maciej Wołczyk, Błażej Osiński, Peter Ondruska
In this work we are the first to present an offline policy gradient method for learning imitative policies for complex urban driving from a large corpus of real-world demonstrations.
no code implementations • 16 Dec 2020 • Błażej Osiński, Piotr Miłoś, Adam Jakubowski, Paweł Zięcina, Michał Martyniak, Christopher Galias, Antonia Breuer, Silviu Homoceanu, Henryk Michalewski
This work introduces interactive traffic scenarios in the CARLA simulator, which are based on real-world traffic.
no code implementations • ICLR 2020 • Łukasz Kaiser, Mohammad Babaeizadeh, Piotr Miłos, Błażej Osiński, Roy H. Campbell, Konrad Czechowski, Dumitru Erhan, Chelsea Finn, Piotr Kozakowski, Sergey Levine, Afroz Mohiuddin, Ryan Sepassi, George Tucker, Henryk Michalewski
We describe Simulated Policy Learning (SimPLe), a complete model-based deep RL algorithm based on video prediction models and present a comparison of several model architectures, including a novel architecture that yields the best results in our setting.
1 code implementation • 29 Nov 2019 • Błażej Osiński, Adam Jakubowski, Piotr Miłoś, Paweł Zięcina, Christopher Galias, Silviu Homoceanu, Henryk Michalewski
Using reinforcement learning in simulation and synthetic data is motivated by lowering costs and engineering effort.
2 code implementations • 2 Apr 2018 • Łukasz Kidziński, Sharada Prasanna Mohanty, Carmichael Ong, Zhewei Huang, Shuchang Zhou, Anton Pechenko, Adam Stelmaszczyk, Piotr Jarosik, Mikhail Pavlov, Sergey Kolesnikov, Sergey Plis, Zhibo Chen, Zhizheng Zhang, Jiale Chen, Jun Shi, Zhuobin Zheng, Chun Yuan, Zhihui Lin, Henryk Michalewski, Piotr Miłoś, Błażej Osiński, Andrew Melnik, Malte Schilling, Helge Ritter, Sean Carroll, Jennifer Hicks, Sergey Levine, Marcel Salathé, Scott Delp
In the NIPS 2017 Learning to Run challenge, participants were tasked with building a controller for a musculoskeletal model to make it run as fast as possible through an obstacle course.