Summary of Massively Multiagent Minigames For Training Generalist Agents, by Kyoung Whan Choe et al.
Massively Multiagent Minigames for Training Generalist Agents
by Kyoung Whan Choe, Ryan Sullivan, Joseph Suárez
First submitted to arxiv on: 7 Jun 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Machine Learning (cs.LG); Multiagent Systems (cs.MA)
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 Meta MMO is a collection of multi-agent minigames designed to test reinforcement learning models. Built upon Neural MMO, a previously used environment, Meta MMO adds several computationally efficient games. The paper explores the ability of AI agents to generalize across different minigames using a single set of weights. The authors release the environment, baselines, and training code under the MIT license, hoping it will foster further progress in this area and serve as a benchmark for many-agent generalization. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Meta MMO is a special kind of game that helps train artificial intelligence (AI) agents to work well with many other AI agents. It’s like a big playground where agents learn to play different games. The researchers created several new games that are easy to compute, and they tested how well the AI agents could learn to play all these games using just one set of instructions. They’re sharing their game environment, practice runs, and training code so other people can use it to improve AI. |
Keywords
» Artificial intelligence » Generalization » Reinforcement learning
