Self-Play is a training method where an agent plays against a copy of itself or against previous versions to develop and improve strategies.
Through this method, the agent can learn without external data by generating its own experiences and learning from them.
Self-Play is often used in combination with reinforcement learning to develop strong game strategies in games with high strategic demands, such as Go or Poker.
Important techniques include training against oneself in an iterative process, using so-called "Nash equilibria," and avoiding overfitting to previous versions.
Self-Play enables the discovery of complex behaviors that are not easily found through conventional learning from example data.