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From paper Sven cites:"Our proposed method achieves a
success rate of 97.4%"Wow. That is....solved! I'm not sure I understand the mechanism used by their agent, though. " Our approach simply applies the trained policy tochoose optimal actions in the reCAPTCHA environment."" Depending on this interaction, the reCAPTCHAsystem will reward the user with a score."I do not see how they establish this scoring, as reCAPTCHA systems only reward 100% for solves, and not anything for partial solves.So then, how does the "trained policy" run through the time/direction/velocity (last parameter just to authenticate a real user, not to really solve puzzle, I would guess) maze of simulated mouse movements, in such a way as to solve the reCAPTCHA puzzle?Just do many, many, many, many puzzles and correlate results with which ones solve correctly, then "see" what is in each "block" that is geometrically or color-wise similar?An incredibly large sample size is then needed??Just wondering....