So this started as a dumb idea and somehow turned into a full simulation. We wanted to see if we could get a rat to “play” GTA-style gameplay using a brain-computer interface. Obviously giving a rat a full controller and expecting it to understand the open world is ridiculous, so we stripped everything down. it works: Freddy has a multi-channel neural interface (simulated in our case, but based on real rodent BCI work). An adaptive decoder turns his brain activity into a tiny set of actions: How Forward Left Right Interact Boost That’s it. No aiming, no full driving physics, no inventory. Just enough to move around and interact with highlighted objects.The reward system is pure rat logic: enter a car = 1 treat Complete a short drive = 2 treats Complete a longer drive = 3.5 treats Successfully evade detection = 2.5 treats Sitting idle = small penalty The second he completes one of these, he gets the treat. Freddy didn’t care about story or chaos they care about consistent rewards. So Freddy very quickly figured out that “go to glowing car interact drive a bit” was the highest payout loop. We also track heart rate, stress, and arousal during the session. If he gets too stressed, the effective reward drops a bit so he doesn’t just mash actions. The decoder adapts online based on which neural patterns actually lead to treats, so over a session he gets better at producing the patterns that pay out. What actually happens:Freddy ignores most of the “open world” and just loops the highest-reward sequences. Longer missions only happen when the short ones stop paying as well. Repo is up if anyone wants to look at the messy adaptive decoder and reward shaping. Repo: https://github.com/wtb3771c30h/Rat-Theft-Auto submitted by /u/Sad_Measurement_7502
Originally posted by u/Sad_Measurement_7502 on r/ArtificialInteligence
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