Balancing the AI Strength of Roles in Self-Play Training with Regret Matching+
Fuente:
arXiv
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| Autor principal: | |
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| Formato: | Preprint |
| Publicado: |
2024
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866913218379644928 |
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| author | Wang, Xiaoxi |
| author_facet | Wang, Xiaoxi |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2401_12557 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Balancing the AI Strength of Roles in Self-Play Training with Regret Matching+ Wang, Xiaoxi Artificial Intelligence 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. |
| title | Balancing the AI Strength of Roles in Self-Play Training with Regret Matching+ |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2401.12557 |