Self-Play

A training technique in reinforcement learning where a model improves by competing against copies of itself rather than against human opponents or a fixed environment. Self-play generates an open-ended stream of training experience that scales with the model's own capabilities, an approach that enabled AlphaGo and AlphaZero to reach superhuman performance in games with no human data beyond the rules.