The deceptively simple running game is so challenging to master that even an AI trained using machine learning still only mustered a top 10 score instead of shattering the record.
If you’ve never played QWOP before, you owe it to yourself to give it a try and see if you can even get your sprinter off the starting line. Developed by Bennett Foddy back in 2008, QWOP was inspired by an ‘80s arcade game called Track & Field that requires players to mindlessly mashing buttons to win a race. QWOP takes a different approach and instead has players use four keys to control the individual movements of a runner’s thighs and calves—a runner who behaves like a floppy rag doll and is subject to real-world physics, including the effects of gravity. It might sound simple, but mastering the timing and cadence of the key presses needed to get the sprinter to just awkwardly move forward can be incredibly frustrating.
But even with access to the best possible playing techniques, Liao found the best results came from a machine learning training regimen that involved 25 hours of the AI playing by itself, 15 hours learning from the data gleaned from Kurodo’s expert runs, and another 25 hours of self-play.
But even with all that effort, the QWOP-playing AI’s best 100-meter dash result had it crossing the finish line in 1 minute and 8 seconds—a top 10 finish. According to Speedrun.com, the current 100-meter dash world record is a mere 48 seconds, set just a month ago. Liao is confident with more training and a different reward system (how the AI learns it’s done something correctly), setting a QWOP world record could eventually happen, although since it’s a computer playing the game the record may never be officially acknowledged.
An AI was taught to play the world’s most difficult video game and still could not set a new record