What is really interesting to me is to train the game models from the existing player base. This is fairly common but more simulation replaying in networked ga

What is really interesting to me is to train the game models from the existing player base. This is fairly common but more simulation replaying in networked games with fake bots based on that gameplay. Previously I have done similar in networked games where the way points and reactions are reused in AI agents that mimic players. The networked players that we captured, if another player dropped, we replayed captured ghost plays essentially with more interactivity that seem more real than just AI. This allowed us to have networking game experience of matchup, that can drop a player, but seemingly did not but still have that player act human not just bot-like. This is now used in all our matchup/networking games because it makes the matchup/networked experience better for the player even if they end up playing a sim and not knowing it. Taking that further if you could train the machine learning models from the existing player base for an additional level of focus it could really make the game fun and you could use it to even drive player behavior. Machine learning definitely has a place in game testing and progression tuning. However, even more it would be nice if it can learn on the fly and not end up in predictable AI, one possible way would be capturing actual human player behavior and modeling that.