<ul><li>The Mean-Field Schrodinger Bridge (MFSB) problem aims to find the minimum effort control policy for a swarm of cooperative agents.</li><li>New efficient parameterization is proposed to approximate MFSB solutions for Gaussian Mixture Model boundary distributions.</li><li>The proposed approach uses a mixture of elementary policies to solve a Gaussian-to-Gaussian Covariance Steering problem.</li><li>The method can handle probabilistic hard constraints and is applied to various numerical examples.</li></ul>

Steering Large Agent Populations using Mean-Field Schrodinger Bridges with Gaussian Mixture Models

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