Self-Play
Definition
Self-play generates learning experiences through interaction with own model variants.
A training method where an agent generates experience by interacting with copies, earlier versions, or co-learning variants of its own strategy. It is especially suitable for games and multi-agent environments with clear rules.
Simply explained
An agent trains in a regulated environment against copies or earlier versions of its own strategy.
Why it matters in practice
The term helps to select, develop, evaluate AI systems in a well-founded manner and realistically assess their technical risks.
Example from business
A planning agent trains in a simulation against earlier versions of its policy.
Limits and pitfalls
- Treating self-play as a universal method for single-agent problems, equating simulation with reality, losing opponent diversity, or overlooking cyclical and exploitable strategies.
Origin & history
Self-play is known from game theory and was made prominent by TD-Gammon, AlphaGo, and AlphaZero.
Use in mid-sized companies
- Possible application: A planning agent trains in a simulation against earlier versions of its policy.
Frequently asked questions
What does self-play mean?
A training procedure in which an agent generates experience by interacting with copies, earlier versions, or co-learning variants of its own strategy. It is particularly suitable for games and multi-agent environments with clear rules. What mistakes should be avoided? Treating self-play as a universal method for single-agent problems, equating simulation with reality, losing opponent diversity, or overlooking cyclical and exploitable strategies.
Sources
Source checked on 10.10.2026. Editorial application examples are hypothetical; no evidence of real customer results.
Editorially reviewed on: