Yes, if it’s as simple as ‘spam clicks from imitation learning are too hard to wash out via self-play given the weak APM limits’, it should be relatively easy to fix. Add a very tiny penalty for each click to incentivize efficiency, or preprocess the replay dataset—if a ‘spam click’ does nothing useful, it seems like it should be possible to replay through all the games, track what clicks actually result in a game-play difference and what clicks are either idempotent (eg multiple clicks in the same spot) or cancel out (eg a click to go one place which is replaced by a click to go another place before the unit has moved more than epsilon distance), and filter out the spam clicks.
Yes, if it’s as simple as ‘spam clicks from imitation learning are too hard to wash out via self-play given the weak APM limits’, it should be relatively easy to fix. Add a very tiny penalty for each click to incentivize efficiency, or preprocess the replay dataset—if a ‘spam click’ does nothing useful, it seems like it should be possible to replay through all the games, track what clicks actually result in a game-play difference and what clicks are either idempotent (eg multiple clicks in the same spot) or cancel out (eg a click to go one place which is replaced by a click to go another place before the unit has moved more than epsilon distance), and filter out the spam clicks.