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Aleksander Czechowski
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Journal Articles
- 2023
- [j2]Aleksander Czechowski, Georgios Piliouras:
Non-chaotic limit sets in multi-agent learning. Auton. Agents Multi Agent Syst. 37(2): 29 (2023) - 2016
- [j1]Aleksander Czechowski, Piotr Zgliczynski:
Existence of Periodic Solutions of the FitzHugh-Nagumo Equations for an Explicit Range of the Small Parameter. SIAM J. Appl. Dyn. Syst. 15(3): 1615-1655 (2016)
Conference and Workshop Papers
- 2023
- [c5]Aleksander Czechowski, Frans A. Oliehoek:
Safety Guarantees in Multi-agent Learning via Trapping Regions. AAMAS 2023: 2403-2405 - [c4]Aleksander Czechowski, Frans A. Oliehoek:
Safe Multi-agent Learning via Trapping Regions. IJCAI 2023: 82-90 - 2022
- [c3]Aleksander Czechowski, Georgios Piliouras:
Poincaré-Bendixson Limit Sets in Multi-Agent Learning. AAMAS 2022: 318-326 - [c2]Vibhav Inna Kedege, Aleksander Czechowski, Ludo Stellingwerff, Frans A. Oliehoek
:
Multi Robot Surveillance and Planning in Limited Communication Environments. ICAART (1) 2022: 139-147 - 2020
- [c1]Aleksander Czechowski, Frans A. Oliehoek
:
Decentralized MCTS via Learned Teammate Models. IJCAI 2020: 81-88
Informal and Other Publications
- 2023
- [i5]Aleksander Czechowski, Frans A. Oliehoek:
Safety Guarantees in Multi-agent Learning via Trapping Regions. CoRR abs/2302.13844 (2023) - 2022
- [i4]Viktor Zobernig, Richard A. Saldanha, Jinke He, Erica van der Sar, Jasper van Doorn, Jia-Chen Hua, Lachlan Robert Mason, Aleksander Czechowski, Drago Indjic, Tomasz Kosmala, Alessandro Zocca, Sandjai Bhulai, Jorge Montalvo Arvizu, Claude Klöckl, John Moriarty:
RangL: A Reinforcement Learning Competition Platform. CoRR abs/2208.00003 (2022) - 2021
- [i3]Aleksander Czechowski, Georgios Piliouras:
Poincaré-Bendixson Limit Sets in Multi-Agent Learning. CoRR abs/2102.00053 (2021) - 2020
- [i2]Aleksander Czechowski, Frans A. Oliehoek:
Decentralized MCTS via Learned Teammate Models. CoRR abs/2003.08727 (2020) - 2019
- [i1]Miguel Suau, Elena Congeduti, Rolf Starre, Aleksander Czechowski, Frans A. Oliehoek:
Influence-aware Memory for Deep Reinforcement Learning. CoRR abs/1911.07643 (2019)
Coauthor Index
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