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When training artificial intelligence for games encompassing multiple roles,the development of a generalized model capable of controlling any characterwithin the game presents a viable option. This strategy not only conservescomputational resources and time during the training phase but also reducesresource requirements during deployment. training such a generalized modeloften encounters challenges related to uneven capabilities when controllingdifferent roles. A simple method is introduced based on Regret Matching+, whichfacilitates a more balanced performance of strength by the model whencontrolling various roles.

Balancing the AI Strength of Roles in Self-Play Training with Regret Matching+