AlphaZero/MuZero-style learning to sequential, perfect information, non-zero sum board games

Hello!

I am looking for research that has successfully applied AlphaZero/MuZero-style learning to sequential, perfect information, non-zero sum board games, e.g. Terra Mystica where the winning player is decided by a numerical score (associated with each player) at the end of the game, rather than the zero sum outcomes of games such as Chess, Shogi, Go, etc.

I figure there must exist an approach that works for multi-agent (> 2 player) games.

Any suggestions?

Thank you