Article

A Dynamic Game Learning Framework for Cooperative Iterative Tasks

This study proposes a general learning framework for nonlinear, nonconvex dynamic games, applied to multi-agent cooperative iterative tasks with shared constraints. It uses previously executed feasible trajectories to construct a sampled joint safe set and player-specific cost-to-go functions.

Part of the event: A Dynamic Game Learning Framework for Collaborative Iterative Tasks

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