4 papers
Approximately Solving Continuous-Time Mean Field Games with Finite State Spaces
Yannick Eich, Christian Fabian, Kai Cui +1
Mean field games (MFGs) offer a powerful framework for modeling large-scale multi-agent systems. This paper addresses MFGs formulated in continuous time with discrete state spaces,…
Learning Mean Field Control on Sparse Graphs
Christian Fabian, Kai Cui, Heinz Koeppl
Large agent networks are abundant in applications and nature and pose difficult challenges in the field of multi-agent reinforcement learning (MARL) due to their computational and…
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…
Major-Minor Mean Field Multi-Agent Reinforcement Learning
Kai Cui, Christian Fabian, Anam Tahir +1
Multi-agent reinforcement learning (MARL) remains difficult to scale to many agents. Recent MARL using Mean Field Control (MFC) provides a tractable and rigorous approach to otherw…