MA-BC partitions divergent expert data and pools non-conflicting pairs to achieve faster convergence to Pareto-optimal policies in MOMDPs, with a matching minimax lower bound.
Coordinated multi-agent imitation learning
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PlayGen-MoG uses a shared Mixture-of-Gaussians head across agents plus relative attention to generate diverse coordinated plays from a single static formation, achieving 1.68 yard ADE and 3.98 yard FDE with full mixture utilization on football data.
citing papers explorer
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Split the Differences, Pool the Rest: Provably Efficient Multi-Objective Imitation
MA-BC partitions divergent expert data and pools non-conflicting pairs to achieve faster convergence to Pareto-optimal policies in MOMDPs, with a matching minimax lower bound.
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PlayGen-MoG: Framework for Diverse Multi-Agent Play Generation via Mixture-of-Gaussians Trajectory Prediction
PlayGen-MoG uses a shared Mixture-of-Gaussians head across agents plus relative attention to generate diverse coordinated plays from a single static formation, achieving 1.68 yard ADE and 3.98 yard FDE with full mixture utilization on football data.