12 citations · 30 across the 23 of their papers we have counts for
8 papers · 1 filter
Mean Field Games on Weighted and Directed Graphs via Colored Digraphons
Christian Fabian, Kai Cui, Heinz Koeppl
The field of multi-agent reinforcement learning (MARL) has made considerable progress towards controlling challenging multi-agent systems by employing various learning methods. Num…
A Survey on Large-Population Systems and Scalable Multi-Agent Reinforcement Learning
Kai Cui, Anam Tahir, Gizem Ekinci +4
The analysis and control of large-population systems is of great interest to diverse areas of research and engineering, ranging from epidemiology over robotic swarms to economics a…
Optimal Offloading Strategies for Edge-Computing via Mean-Field Games and Control
Kai Cui, Mustafa Burak Yilmaz, Anam Tahir +2
The optimal offloading of tasks in heterogeneous edge-computing scenarios is of great practical interest, both in the selfish and fully cooperative setting. In practice, such syste…
Scalable Task-Driven Robotic Swarm Control via Collision Avoidance and Learning Mean-Field Control
Kai Cui, Mengguang Li, Christian Fabian +1
In recent years, reinforcement learning and its multi-agent analogue have achieved great success in solving various complex control problems. However, multi-agent reinforcement lea…
Learning Sparse Graphon Mean Field Games
Christian Fabian, Kai Cui, Heinz Koeppl
Although the field of multi-agent reinforcement learning (MARL) has made considerable progress in the last years, solving systems with a large number of agents remains a hard chall…
Learning Mean-Field Control for Delayed Information Load Balancing in Large Queuing Systems
Anam Tahir, Kai Cui, Heinz Koeppl
Recent years have seen a great increase in the capacity and parallel processing power of data centers and cloud services. To fully utilize the said distributed systems, optimal loa…
Hypergraphon Mean Field Games
Kai Cui, Wasiur R. KhudaBukhsh, Heinz Koeppl
We propose an approach to modelling large-scale multi-agent dynamical systems allowing interactions among more than just pairs of agents using the theory of mean field games and th…
Motif-based mean-field approximation of interacting particles on clustered networks
Kai Cui, Wasiur R. KhudaBukhsh, Heinz Koeppl
Interacting particles on graphs are routinely used to study magnetic behaviour in physics, disease spread in epidemiology, and opinion dynamics in social sciences. The literature o…