Summary of Transformer Guided Coevolution: Improved Team Selection in Multiagent Adversarial Team Games, by Pranav Rajbhandari and Prithviraj Dasgupta and Donald Sofge
Transformer Guided Coevolution: Improved Team Selection in Multiagent Adversarial Team Games
by Pranav Rajbhandari, Prithviraj Dasgupta, Donald Sofge
First submitted to arxiv on: 17 Oct 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Multiagent Systems (cs.MA); Neural and Evolutionary Computing (cs.NE)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary We investigate the challenge of selecting the most effective teams in multiagent adversarial games, where agents need to collaborate and adapt to each other. Our proposed solution, BERTeam, leverages a transformer-based deep neural network trained using Masked Language Model techniques to identify the best team composition from a pool of trained individual players. This is combined with coevolutionary deep reinforcement learning, which trains diverse players for selection. We test our approach in the Marine Capture-The-Flag game and observe that BERTeam learns non-trivial team compositions that perform well against unseen opponents. Notably, we find that BERTeam outperforms MCAA, a comparable algorithm that optimizes team selection. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine playing a team sport where you need to work together with your friends to win. But what if you could choose the best players for your team? That’s exactly what this paper is about: finding the perfect team in a game where teams compete against each other. We developed an algorithm called BERTeam that helps choose the right players from a group of trained individuals. We tested it in a game called Marine Capture-The-Flag and found that our algorithm can pick teams that do well even against opponents they’ve never seen before. It’s better than another similar approach we tried! |
Keywords
» Artificial intelligence » Masked language model » Neural network » Reinforcement learning » Transformer
