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Found 6 papers, 1 papers with code
Learning Mean Field Games on Sparse Graphs: A Hybrid Graphex Approach
This hybrid graphex learning approach leverages that the system mainly consists of a highly connected core and a sparse periphery.
Learning Decentralized Partially Observable Mean Field Control for Artificial Collective Behavior
However, multi-agent RL (MARL) remains a challenge in terms of decentralization, partial observability and scalability to many agents.
Major-Minor Mean Field Multi-Agent Reinforcement Learning
The algorithm is shown to approximate the policy gradient of the underlying M3FC MDP.
Scalable Task-Driven Robotic Swarm Control via Collision Avoidance and Learning Mean-Field Control
Thus, we combine collision avoidance and learning of mean-field control into a unified framework for tractably designing intelligent robotic swarm behavior.
Mean Field Games on Weighted and Directed Graphs via Colored Digraphons
Graphon mean field games (GMFGs) on the other hand provide a scalable and mathematically well-founded approach to learning problems that involve a large number of connected agents.
Learning Sparse Graphon Mean Field Games
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 challenge.
