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researcher

Kai Cui

Technische Universität Darmstadt

26 papers hereh-index 8332 citations33 works total

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • first author11
  • middle author13
  • last author1

Across the 25 of 26 papers where every author was matched, so the position is known.

fields
  • cs.MA8
  • cs.GT5
  • cs.DC3
  • cs.LG3
  • cs.RO3
  • cond-mat.soft1
affiliations
  • Technische Universität Darmstadt
Homepage
same name
  • Kai Cui — 1 paper, h 1
  • Kai Cui — 1 paper
  • Kai Cui — 1 paper, h 5
  • Kai Cui — 1 paper, h 2
  • Kai Cui — 1 paper

Either other researchers who publish under this name, or the same person where the external sources have not merged their records.

identity via Semantic Scholar / OpenAlex

activity
20212026
most citedApproximately Solving Mean Field Games via Entropy-Regularized Deep Reinforcement Learning

12 citations · 30 across the 23 of their papers we have counts for

collaborators
Showing 2024Show all

4 papers · 1 filter

cs.GT2024

Bounded Rationality Equilibrium Learning in Mean Field Games

Yannick Eich, Christian Fabian, Kai Cui +1

Mean field games (MFGs) tractably model behavior in large agent populations. The literature on learning MFG equilibria typically focuses on finding Nash equilibria (NE), which assu…

eess.SY2024

FPGA-Based Neural Thrust Controller for UAVs

Sharif Azem, David Scheunert, Mengguang Li +4

The advent of unmanned aerial vehicles (UAVs) has improved a variety of fields by providing a versatile, cost-effective and accessible platform for implementing state-of-the-art al…

cs.RO2024

A Modular Aerial System Based on Homogeneous Quadrotors with Fault-Tolerant Control

Mengguang Li, Kai Cui, Heinz Koeppl

The standard quadrotor is one of the most popular and widely used aerial vehicle of recent decades, offering great maneuverability with mechanical simplicity. However, the under-ac…

cs.MA2024

Learning Mean Field Games on Sparse Graphs: A Hybrid Graphex Approach

Christian Fabian, Kai Cui, Heinz Koeppl

Learning the behavior of large agent populations is an important task for numerous research areas. Although the field of multi-agent reinforcement learning (MARL) has made signific…

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Not affiliated with arXiv. Researcher data from Semantic Scholar (ODC-BY) and OpenAlex.