3 ms·

Would be interesting to get your hands on a large set of replays from a lot of gosu players and run some machine learning offline prior to each map for the curr

• TulliusCicero 11y ago
  > Would be interesting to get your hands on a large set of replays from a lot of gosu players and run some machine learning offline prior to each map for the current match map. There are probably too many variables in Starcraft for this to be practical. With a game like chess there's a fairly obvious set of state variables to keep track of (max of 32 pieces that each have a position in essentially an 8x8 2D array), with Starcraft it's pretty nuts though, each side can have dozens of buildings, dozens of units of ~15 types, each of which has a position on a much larger grid, health, sometimes energy, status effects, etc. Obviously there are RTS games that are more complicated yet, but the jump from a turn-based board game to an RTS is just huge.
  • Zolomon 11y ago
    I am fully aware of the complexity of the game. That's why it would be interesting to see how much can be inferred from offline learning first. For example, looking at unit positions over time for the map can be quite rewarding, especially between the different phases of the game (early, mid, late).
• krasi0 11y ago
  The author of the winning bot (tscmoo) is currently experimenting with training NNs on data acquired by parsing replays of pro-level games. He's been mostly focusing on automatic learning of build orders (BOs) in specific match ups.