Self-Play Training

Definition

Self-Play Training is training method where agents improve by competing or cooperating against versions of themselves. In defense applications, it generates tactical variation without waiting for every adversary behavior to be hand-authored. The hard part is exploit cycles, unrealistic equilibria, and overfitting to self-generated opponents, especially when systems are deployed across contested links, coalition boundaries, and mixed human-machine teams. KhanBMS treats it as a red-blue rehearsal engine inside KhanBMS simulation pipelines, tying the concept back to modular command, edge execution, and auditable authority.

Reference attributes

Layer

training curriculum method

Operational value

Generates tactical variation without waiting for every adversary behavior to be hand-authored

Primary risk

Exploit cycles, unrealistic equilibria, and overfitting to self-generated opponents

KhanBMS role

A red-blue rehearsal engine inside KhanBMS simulation pipelines

Related terms

• League-Based Training
  Self-play method that maintains a population of opponents and teammates to improve robustness.
• AI Wargaming
  Use of AI agents and simulations to explore adversary moves, blue responses, and campaign dynamics.
• Multi-Agent Reinforcement Learning (MARL)
  Reinforcement-learning framework where multiple agents learn cooperative or adversarial behavior together.
• Counter-AI Operations
  Actions that detect, disrupt, deceive, or exploit adversary AI systems and data pipelines.