What ’s the hardest video game you ’ve ever played ? If it was n’t QWOP then let me tell you right know that you do n’t hump how truly unmanageable a game can be . The deceptively dewy-eyed running game is so challenging to control that even anAI train using machine learningstill only mustered a top 10 score instead of shattering the record .
If you ’ve neverplayed QWOPbefore , you owe it to yourself togive it a tryand see if you could even get your sprinter off the start melodic phrase . Developed byBennett Foddyback in 2008 , QWOP was pep up by an ‘ LXXX colonnade plot call Track & Field that requires player to mindlessly mash buttons to win a slipstream . QWOP take a different approach and instead has players use four keys to control the individual movements of arunner ’s thighs and calves — a runner who behaves like a floppy rag doll and is subject to substantial - worldly concern physics , including the effects of gravity . It might sound mere , but master the timing and cadence of the central presses necessitate to get the sprinter to just awkwardly move frontward can be incredibly frustrative .
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Gif:YouTube - Wesley Liao
Wesley Liao was curious how well a tool like AI , which has been trained to do affair likerealistically reanimate old photosof deceased loved ones , would do spiel QWOP . After first creating a Javascript adapter that would allow an AI tool to really play and interact with the plot , Liao ’s first attack at machine learning merely had the AI playing the secret plan by itself and learning which actions resulted in positive resultant ( the sprinter moving forwards and increasing its speed ) and which ones resulted in negative result ( the sprinter ’s body bending too close to the earth . ) Through this approach the AI learned a “ knee - skin ” technique that would successfully get it across the 100 - meter finale channel , but not at book - specify speeds .
Liao ’s next try at take aim an AI model require memorialize gameplay videos of them trying to win at the game , including the role of longer leg strides which are crucial for increase speeds and crossing the finish dividing line with a seemly prison term . The approach was slightly more successful , but the AI was n’t able to master a special technique used by advanced QWOP players that involves an upward , forward swing of the stage to generate additional impulse .
finally Liao reached out to a seasoned player known as Kurodo ( @cld_elon Twitter ) , one of the top QWOP speed runners in the world , who recorded 50 videos of themselves play the game at an expert spirit level . But even with admission to the best possible performing technique , Liao feel the best results came from a machine memorize training regimen that involved 25 time of day of the AI playing by itself , 15 hours larn from the data gleaned from Kurodo ’s expert runs , and another 25 hr of self - play .
But even with all that effort , the QWOP - playing AI ’s best 100 - cadence dash final result had it crossing the finish line in 1 minute and 8 seconds — a top 10 finishing . accord toSpeedrun.com , the current 100 - time sprint world record is a mere 48 seconds , set just a month ago . Liao is positive with more training and a unlike reward system ( how the AI learns it ’s done something correctly ) , setting a QWOP world record could finally happen , although since it ’s a estimator playing the game the record may never be officially acknowledge .
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