Self-Play

A training paradigm where agents improve by competing or collaborating with copies of themselves, generating training data through their own interactions. In robotics, self-play is used for multi-agent tasks (competitive object manipulation, adversarial robustness), and for generating diverse behavioral strategies. The agent learns robust policies because its training opponents continuously improve.