3 citations · 4 across the 7 of their papers we have counts for
7 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…
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…
Learning Decentralized Partially Observable Mean Field Control for Artificial Collective Behavior
Kai Cui, Sascha Hauck, Christian Fabian +1
Recent reinforcement learning (RL) methods have achieved success in various domains. However, multi-agent RL (MARL) remains a challenge in terms of decentralization, partial observ…
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…
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…