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For example, the system started assuming that relevant actors stay relevant for a small amount of time, and assuming that attached actors share their parent’s visibility. So if a player kişi see another character, the system doesn’t bother checking if they güç see their weapon. These changes decreased the amount of calculations the system performed significantly making it even faster.
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I also found that the Unreal Engine replication system was eventually consistent, so once information became relevant, the state of enemy players would catch up. At this point, I was growing more confident that this was a feasible solution. I figured we could combine these networking features with Unreal Engine’s raycasts for line-of-sight read more checks, and we would be in business.
This was addressed throughout the year by automated actions. We focused on accuracy above all for these actioning waves, and we want to increase their cadence in the future (more on that below).
Before implementing the occlusion culling solution, our server performance was awful, Frame times were doubled, which basically sent us from 128 tick back to 64 tick. In a 10 player game, we were calculating relevancy more than 350 times every frame, and each one of those included the relatively costly original 10 raycast line-of-sight checks. The new PVS-based checks dramatically improved performance and I gained even more through a few other optimizations.
It's clear that Riot is listening, though it hasn't shown any interest in changing the basic workings of its anti-cheat software.