arXiv Artificial Intelligence

ROTATE: Regret-driven Open-ended Training for Ad Hoc Teamwork

1 hour ago 11.09.2026 07:00 👁 2 views
ROTATE: Regret-driven Open-ended Training for Ad Hoc Teamwork

Quick summary

arXiv:2505.23686v3 Announce Type: replace Abstract: Learning to collaborate with previously unseen partners is a fundamental generalization challenge, known as Ad Hoc Teamwork (AHT). Existing methods often adopt a two-stage pipeline: first, a fixed population of teammates is generated, and second, an AHT agent is trained to collaborate with them. This separation limits coverage of behaviors and ignores whether the generated teammates are informative for the AHT agent to learn from. On the other hand, AHT agents are typically trained under the assumption that the training teammate set is uncont

Key takeaways

  • arXiv:2505.23686v3 Announce Type: replace Abstract: Learning to collaborate with previously unseen partners is a fundamental generalization challenge, known as Ad Hoc Teamwork (AHT).
  • Existing methods often adopt a two-stage pipeline: first, a fixed population of teammates is generated, and second, an AHT agent is trained to collaborate with them.
  • This separation limits coverage of behaviors and ignores whether the generated teammates are informative for the AHT agent to learn from.

Why it matters

“ROTATE: Regret-driven Open-ended Training for Ad Hoc Teamwork” illustrates how changes in the AI ecosystem can affect products, workflows and user expectations together. Its lasting significance depends on measurable adoption, cost and safety outcomes.

Kaynak sitede devamını oku: arXiv Artificial Intelligence ↗