Summary of Alphavit: a Flexible Game-playing Ai For Multiple Games and Variable Board Sizes, by Kazuhisa Fujita
AlphaViT: A Flexible Game-Playing AI for Multiple Games and Variable Board Sizes
by Kazuhisa Fujita
First submitted to arxiv on: 25 Aug 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
GrooveSquid.com Paper Summaries
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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 The proposed AlphaViT, AlphaViD, and AlphaVDA game-playing AI agents are based on the AlphaZero framework, enhanced with Vision Transformer (ViT). These agents play multiple board games of various sizes using a single network with shared weights. AlphaViT employs only a transformer encoder, whereas AlphaViD and AlphaVDA incorporate both transformer encoders and decoders. The additional decoder layers in AlphaViD and AlphaVDA provide flexibility to adapt to various action spaces and board sizes. Experimental results show that the proposed agents outperform traditional algorithms such as Minimax and Monte Carlo Tree Search and approach the performance of AlphaZero, despite using a single deep neural network (DNN) with shared weights. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper introduces new game-playing AI agents based on AlphaZero. These agents can play many different board games using one set of rules. They use something called Vision Transformer (ViT), which helps them understand the game better. The agents are tested and shown to be better than other algorithms at playing games like chess and checkers. They even work well when trained to play multiple games at once. |
Keywords
» Artificial intelligence » Decoder » Encoder » Neural network » Transformer » Vision transformer » Vit
