Summary of Codreamer: Communication-based Decentralised World Models, by Edan Toledo et al.
CoDreamer: Communication-Based Decentralised World Models
by Edan Toledo, Amanda Prorok
First submitted to arxiv on: 19 Jun 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 The paper introduces CoDreamer, an extension of the Dreamer algorithm for multi-agent environments in reinforcement learning. The goal is to improve sample efficiency by leveraging Graph Neural Networks for a two-level communication system that tackles partial observability and inter-agent cooperation. This system separately utilises communication within learned world models and policies for each agent to enhance modelling and task-solving. Results show CoDreamer offers greater expressive power than a naive application of Dreamer, outperforming baseline methods across various multi-agent environments. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary CoDreamer is a new way to help robots learn how to work together in complex situations. Right now, it’s hard for computers to figure out what other agents are doing or thinking, which makes it tough for them to work together effectively. CoDreamer uses special computer programs called Graph Neural Networks to help the agents communicate and understand each other better. This makes it easier for them to solve problems and achieve their goals. The new algorithm is tested in different scenarios and shows that it can do a better job than existing methods. |
Keywords
» Artificial intelligence » Reinforcement learning
