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. Epistemic uncertainty indicates where the policy is least confident and serves as an automatic proxy for scenario difficulty. Exploring agents use this signal to seek out hard cases, generating a challenging curriculum without manual scenario design. The resulting policy outperforms standard self-play, achieving bes...
| Authors | Sajad Marvi, Julian Schmidt, Alessandro Canevaro et al. |
|---|---|
| Primary category | cs.RO |
| Published | 14 October 2026 |
| Last updated at source | 14 October 2026 |
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