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ORCIDRyan Kortvelesy
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Conference and Workshop Papers
- 2024
[c9]Matteo Bettini, Ryan Kortvelesy, Amanda Prorok:
Controlling Behavioral Diversity in Multi-Agent Reinforcement Learning. ICML 2024- 2023
- [c8]Ryan Kortvelesy, Steven D. Morad, Amanda Prorok:
Permutation-Invariant Set Autoencoders with Fixed-Size Embeddings for Multi-Agent Learning. AAMAS 2023: 1661-1669 - [c7]Steven D. Morad
, Ryan Kortvelesy, Matteo Bettini, Stephan Liwicki, Amanda Prorok:
POPGym: Benchmarking Partially Observable Reinforcement Learning. ICLR 2023 - [c6]Ryan Kortvelesy, Steven D. Morad, Amanda Prorok:
Generalised f-Mean Aggregation for Graph Neural Networks. NeurIPS 2023 - [c5]Steven D. Morad, Ryan Kortvelesy, Stephan Liwicki, Amanda Prorok:
Reinforcement Learning with Fast and Forgetful Memory. NeurIPS 2023 - 2022
- [c4]Amanda Prorok, Jan Blumenkamp, Qingbiao Li, Ryan Kortvelesy, Zhe Liu, Ethan Stump:
The Holy Grail of Multi-Robot Planning: Learning to Generate Online-Scalable Solutions from Offline-Optimal Experts. AAMAS 2022: 1804-1808 - [c3]Matteo Bettini, Ryan Kortvelesy, Jan Blumenkamp, Amanda Prorok:
VMAS: A Vectorized Multi-agent Simulator for Collective Robot Learning. DARS 2022: 42-56 - [c2]Steven D. Morad, Stephan Liwicki, Ryan Kortvelesy, Roberto Mecca, Amanda Prorok:
Modeling Partially Observable Systems using Graph-Based Memory and Topological Priors. L4DC 2022: 59-73 - 2021
- [c1]Ryan Kortvelesy, Amanda Prorok:
ModGNN: Expert Policy Approximation in Multi-Agent Systems with a Modular Graph Neural Network Architecture. ICRA 2021: 9161-9167
Informal and Other Publications
- 2024
- [i12]Steven D. Morad, Chris Lu, Ryan Kortvelesy, Stephan Liwicki, Jakob N. Foerster, Amanda Prorok:
Revisiting Recurrent Reinforcement Learning with Memory Monoids. CoRR abs/2402.09900 (2024) - [i11]Matteo Bettini, Ryan Kortvelesy, Amanda Prorok:
Controlling Behavioral Diversity in Multi-Agent Reinforcement Learning. CoRR abs/2405.15054 (2024) - 2023
- [i10]Ryan Kortvelesy, Steven D. Morad, Amanda Prorok:
Permutation-Invariant Set Autoencoders with Fixed-Size Embeddings for Multi-Agent Learning. CoRR abs/2302.12826 (2023) - [i9]Steven D. Morad, Ryan Kortvelesy, Matteo Bettini, Stephan Liwicki, Amanda Prorok:
POPGym: Benchmarking Partially Observable Reinforcement Learning. CoRR abs/2303.01859 (2023) - [i8]Ryan Kortvelesy, Steven D. Morad, Amanda Prorok:
Generalised f-Mean Aggregation for Graph Neural Networks. CoRR abs/2306.13826 (2023) - [i7]Ryan Kortvelesy:
Fixed Integral Neural Networks. CoRR abs/2307.14439 (2023) - [i6]Steven D. Morad, Ryan Kortvelesy, Stephan Liwicki, Amanda Prorok:
Reinforcement Learning with Fast and Forgetful Memory. CoRR abs/2310.04128 (2023) - [i5]Jennifer Gielis, Ajay Shankar, Ryan Kortvelesy, Amanda Prorok:
Modeling Aggregate Downwash Forces for Dense Multirotor Flight. CoRR abs/2312.03488 (2023) - 2022
- [i4]Ryan Kortvelesy, Amanda Prorok:
QGNN: Value Function Factorisation with Graph Neural Networks. CoRR abs/2205.13005 (2022) - [i3]Matteo Bettini, Ryan Kortvelesy, Jan Blumenkamp, Amanda Prorok:
VMAS: A Vectorized Multi-Agent Simulator for Collective Robot Learning. CoRR abs/2207.03530 (2022) - 2021
- [i2]Ryan Kortvelesy, Amanda Prorok:
ModGNN: Expert Policy Approximation in Multi-Agent Systems with a Modular Graph Neural Network Architecture. CoRR abs/2103.13446 (2021) - [i1]Amanda Prorok, Jan Blumenkamp, Qingbiao Li, Ryan Kortvelesy, Zhe Liu, Ethan Stump:
The Holy Grail of Multi-Robot Planning: Learning to Generate Online-Scalable Solutions from Offline-Optimal Experts. CoRR abs/2107.12254 (2021)
Coauthor Index
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