Robot Learning

Self-Play

Self-play is a reinforcement learning technique in which an agent improves by training against copies or past versions of itself, generating an automatic curriculum whose difficulty scales with the agent's own competence. It powered landmark results such as AlphaGo, AlphaZero, and OpenAI Five. In robotics, related asymmetric variants pit a goal-proposing agent against a goal-solving agent, or an adversary that perturbs dynamics against a robust policy.

Why it matters for physical AI

Automatic curricula from self-play sidestep manual task design, and adversarial variants are a practical route to policies robust enough for hardware deployment.