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MSMF-Q: Multi-Scale Mean Field Q-Learning for Scalable Multi-Agent Coordination
This research investigates MSMF-Q, a Multi-Scale Mean Field Q-Learning approach for scalable coordination in large multi-agent systems. The method addresses a limitation of conventional mean-field reinforcement learning, which can compress a heterogeneous population into a single global average, and purely local approaches, which may overlook broader population behavior. MSMF-Q combines an attentive near-field representation of influential neighboring agents with a far-field summary of global action distributions. Initial proof-of-concept experiments evaluate the method across changing population sizes and heterogeneity levels. The research remains in progress, with broader environments, stronger statistical validation, and long-horizon evaluation planned.
Concept explorationExplore the studyFrom the notebook
This research investigates MSMF-Q, a Multi-Scale Mean Field Q-Learning approach for scalable coordination in large multi-agent systems. The method addresses a limitation of conventional mean-field reinforcement learning, which can compress a heterogeneous population into a single global average, and purely local approaches, which may overlook broader population behavior. MSMF-Q combines an attentive near-field representation of influential neighboring agents with a far-field summary of global action distributions. Initial proof-of-concept experiments evaluate the method across changing population sizes and heterogeneity levels. The research remains in progress, with broader environments, stronger statistical validation, and long-horizon evaluation planned.
Concept explorationThis research examines D-MARN-RT, an extension of the D-MARN framework for autonomous value discovery and negotiation in agentic commerce. The work investigates how buyer and merchant agents can discover compatible offers, enforce hard constraints, negotiate transaction terms, and produce verified commitments before checkout and payment.
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