Self-Play refers to a learning paradigm in which an agent A in an environment E plays against itself or against historical versions A' in order to develop optimal strategies through interaction.
Formally, Self-Play can be modeled as a multi-stage, iterative optimization procedure within the framework of Markov decision processes (MDPs) or multi-agent games, where the agent updates its policy \( \pi \) through interaction with \( \pi' \).
Well-known applications include AlphaGo (Silver et al., 2016), where deep reinforcement learning was combined with Monte Carlo tree search and Self-Play to achieve superhuman playing strength.
Self-Play enables reaching equilibrium states (e.g., Nash equilibria) in multi-player scenarios and is particularly effective in environments without existing training data.
The central challenge is to ensure stability and diversity of strategies to prevent learning from falling into suboptimal cycles.
Definition:
“Self-Play is a learning method in which an agent improves its own strategies by playing against itself or against historical versions to learn optimal behaviors in an environment.”
Source:
Silver, D., Huang, A., Maddison, C. J., et al. (2016). Mastering the game of Go with deep neural networks and tree search. Nature, 529(7587), 484–489.