Balancing the AI Strength of Roles in Self-Play Training with Regret Matching+
This addresses a specific issue in multi-role game AI training, but appears incremental as it builds on existing techniques.
The paper tackles the problem of uneven capabilities in a generalized AI model controlling multiple roles in games, and introduces a method based on Regret Matching+ to achieve more balanced performance, though no concrete numbers are provided.
When training artificial intelligence for games encompassing multiple roles, the development of a generalized model capable of controlling any character within the game presents a viable option. This strategy not only conserves computational resources and time during the training phase but also reduces resource requirements during deployment. training such a generalized model often encounters challenges related to uneven capabilities when controlling different roles. A simple method is introduced based on Regret Matching+, which facilitates a more balanced performance of strength by the model when controlling various roles.