In this lesson, you will learn how to optimize your AI systems by moving away from expensive frame-by-frame polling and using transition caching to handle large crowds of agents efficiently.
The Problem with Constant Polling
When you have dozens of AI agents, running Update() logic inside every state—like calculating distances to targets or checking line-of-sight—can quickly cripple your frame rate. A naive approach asks every agent, "Should I change state?" every single frame, even if the result rarely changes.
Efficient State Polling
Instead of checking conditions every frame, we implement a time-sliced update system. By using a timer or a Tick() method, we only evaluate state transitions at a specific frequency (e.g., 5 or 10 times per second).
private float checkInterval = 0.2f;
private float timer;
public void UpdateState(float deltaTime) {
timer -= deltaTime;
if (timer <= 0) {
CheckTransitions(); // Expensive logic here
timer = checkInterval;
}
currentState.Execute(); // Lightweight movement/animation logic
}
Transition Caching
Transition caching involves pre-calculating the "Next State" result and storing it, rather than re-evaluating the transition logic multiple times. If an agent is in a PatrolState, you can cache the AttackState as the result of a TargetInRange transition. By caching the transition destination, you avoid repetitive conditional branches and complex object lookups during the transition phase.
When an event (like an enemy entering the radius) occurs, you trigger the transition once, resolve the cached state, and swap the currentState pointer. This keeps your transition logic clean and predictable.
Batch Processing
To further optimize, consider moving your CheckTransitions calls to a centralized AIController that iterates through an array of agents. By separating the "decision-making" logic (the transition checks) from the "execution" logic (the currentState.Execute()), you can stagger the checks across different frames so that not all 50 agents update their state logic at the same exact time, preventing CPU spikes.
Key Takeaway: By implementing time-sliced polling and caching transition targets, you significantly reduce CPU overhead while maintaining responsive AI behavior.