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

arXiv: https://arxiv.org/abs/2401.12557 · HTML: https://mlsanity.com/paper/2401.12557/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 4 references that are in the map.
- First posted on arXiv 23 Jan 2024.

## Builds on (most cited first, 1 of 1)

- [Solving Large Imperfect Information Games Using CFR+](https://mlsanity.com/paper/1407.5042/) (2014)

## Built on by (most cited first, 1 of 1)

- [Advancing DRL Agents in Commercial Fighting Games: Training, Integration, and Agent-Human Alignment](https://mlsanity.com/paper/2406.01103/) (2024)

## Abstract (arXiv)

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.