Summary of Balancing the Ai Strength Of Roles in Self-play Training with Regret Matching+, by Xiaoxi Wang
Balancing the AI Strength of Roles in Self-Play Training with Regret Matching+
by Xiaoxi Wang
First submitted to arxiv on: 23 Jan 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper proposes a novel approach to training artificial intelligence models for games that involve multiple roles, such as characters with unique abilities or strengths. The authors suggest developing a generalized model that can control any character in the game, which offers several benefits including reduced computational resources and deployment requirements. To address the challenge of uneven capabilities when controlling different roles, the paper introduces Regret Matching+, a simple method that enables more balanced performance by the model. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine you’re playing a game where you have to switch between different characters with unique abilities. This paper shows how to create an artificial intelligence (AI) model that can control any of these characters without needing separate training for each one. This approach saves time and resources when creating the AI, and also makes it more efficient during gameplay. |
