Summary of Putting Data at the Centre Of Offline Multi-agent Reinforcement Learning, by Claude Formanek et al.
Putting Data at the Centre of Offline Multi-Agent Reinforcement Learning
by Claude Formanek, Louise Beyers, Callum Rhys Tilbury, Jonathan P. Shock, Arnu Pretorius
First submitted to arxiv on: 18 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Multiagent Systems (cs.MA)
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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 presents a comprehensive approach to improve data usage and awareness in offline multi-agent reinforcement learning (MARL). It highlights the importance of considering dataset characteristics in achieving state-of-the-art results. The authors survey the literature, revealing that most works generate their own datasets without consistent methodology, leading to sparse information about these datasets. They argue that neglecting dataset nature is problematic, as algorithmic performance is tightly coupled to the used dataset. To address this issue, they provide a clear guideline for generating novel datasets, standardize over 80 existing datasets in a publicly available repository, and develop analysis tools to better understand these datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Offline MARL uses static datasets to find optimal control policies for multi-agent systems. The paper shows how most works generate their own datasets without consistent methodology, providing little information about dataset characteristics. This is problematic because algorithmic performance is closely tied to the used dataset. To fix this, the authors provide guidelines for generating new datasets, standardize 80+ existing datasets in a public repository, and create analysis tools to better understand these datasets. |
Keywords
* Artificial intelligence * Reinforcement learning
