Uncertainty-Driven Self-Play for Robust Multi-Agent Driving Policies
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, which results in a much richer set of behaviors.
Sajad Marvi, Julian Schmidt, Alessandro Canevaro et al.
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