Fascinating! You can see it is trying to mimic many gameplay tactics used by CS:GO players, but it's just barely missing the mark. It's like a monkey trying to mimic human behaviour.
Examples:
- Follows the wall contour using the crosshairs when moving around the corners. The only thing wrong here is that the AI is aiming at the wall instead of at the open space next to the wall where the enemy would be.
- After going through a door, it checks all corner to clear the room. But it's very slow in doing this, and would get shot instantly in a real game.
- Adjusts the crosshair constantly to not look at the floor when walking. The problem is that the crosshair is never at headshot level!
I am not bashing on this AI btw, just pointing out that it's actually getting close to real human behaviour! Fascinating!
Something I've noticed is going well is the fact that the AI doesn't spray n' pray but controls the spray pattern and drags the crosshair down. Shoots in bursts!
> The only thing wrong here is that the AI is aiming at the wall instead of at the open space next to the wall where the enemy would be.
This is actually how well-practiced humans play, aiming instead where the enemies are likely to be (behind the wall) instead of trying to perfectly aim at the corner all the time.
It depends on how you’re planning to peek. Generally you’d want to smoothly track edges to clear out everything, including off-angles. Only if you’re quite certain of an enemy’s position you’ll “aim at the wall”, then peek out. Your cross hair will then be spot on immediately. This method always runs into the risk of overpeeking and getting killed easily. Smoothly tracking edges has the risk of getting swung out wide on, instead of getting peeked at tightly, such that sometimes you want to place your cross hair a bit away from edges, but only if you’re the one holding an angle statically.
Excellent. Believably plays like a pro with regard to spray patterns but maybe slightly slower. This is doom for any cheat detect that relies on outliers. Doesn't look aimbotty in the slightestt.
Well, it's not public knowledge but it seems that VAC does not care about how it looks. Also while it doesn't look aimbotty, according to modern standards it also sucks at the game. In contrast, many pro players look very aimbotty, but are legit. But ultimately yeah, anticheat will be totally lost.
Interesting. What's stopping the AI from more or less instantly detecting human shapes and then dishing out headshots instantly as well? Technically, that should be possible. I suppose its learning material is strictly actual humans, so it'd never be able to learn that, even if technically feasible.
There's already bots that are capable of this (often used for cheating in games) - but this research is trying something more difficult and interesting - which is teaching an AI how to play a game, with nothing but a ton of learning material.
Things like moving around the map in a rational way, learning how to counterplay enemies, tactics when entering a room, all based on solely visual data, are extremely difficult. These skills are much more intuitive to humans, but much harder to learn than "there is a human head at {523,1021}"
Aimbots have been around in FPS games for a very long time, including purely out-of-band approaches (observe video output, send input) as they cannot be detected by anti-cheat software.
They started with a system that lets players review games and try to understand if a player is a cheat or not. Games are anonymized, and you get to rewatch the replay to try to make a determination
They can then use that data to train their NN to help detect which players / games should be fed into this manual review process
I assume now the product is probably good enough that it's ranking reviewers too to determine how good reviewers are at detecting if there's cheats or not.
It's pure genius
Here's a GDC talk you may find interesting:
> In this 2018 GDC session, Valve's John McDonald discusses how Valve has utilized Deep Learning to combat cheating in Counter-Strike: Global Offensive.
https://youtu.be/KTY7UhjIMm4