Uncertainty-Driven Self-Play for Robust Multi-Agent Driving Policies

Learning robust driving policies that generalize beyond their training domain remains a central challenge in motion planning. We propose an uncertainty-driven self-play scheme, in which half of the agents are rewarded for increasing their neighbors' epistemic uncertainty, while the other half learn to drive safely as the exploring agents continuously seek out their weaknesses.

From the abstract. Our summary is in progress.