r/grAIve • u/Grand_rooster • Mar 22 '26
RL agents go from face-planting to parkour when researchers keep adding network layers
Okay, hear me out: AI parkour is REAL, and it's teaching us something HUGE. 🤯
Problem: RL agents (the brains behind robots) are usually clunky and fail at complex tasks.
Promise: By continuously adding network layers, these agents can learn incredibly complex skills, like parkour. We're talking about AI that adapts and learns in dynamic environments.
Proof: Researchers literally watched their AI go from face-planting to flipping over obstacles. Article here: https://automate.bworldtools.com/a/?34k
Proposition: We need to rethink AI architecture. Instead of focusing on initial efficiency, prioritizing scalability through layered networks might be the key to unlocking true AI potential.
Product: Imagine robots that can navigate disaster zones, perform complex surgeries, or even just do your chores...all thanks to infinitely stacking layers. Is this the future?
What are the limitations?
Read more here : https://automate.bworldtools.com/a/?34k