Summary of Dispelling the Mirage Of Progress in Offline Marl Through Standardised Baselines and Evaluation, by Claude Formanek et al.
Dispelling the Mirage of Progress in Offline MARL through Standardised Baselines and Evaluation
by Claude Formanek, Callum Rhys Tilbury, Louise Beyers, Jonathan Shock, Arnu Pretorius
First submitted to arxiv on: 13 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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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 Offline multi-agent reinforcement learning (MARL) has significant potential for real-world applications, but current research is hindered by inconsistencies in baselines and evaluation protocols. This paper identifies shortcomings in existing methodologies and demonstrates that simple, well-implemented baselines can achieve state-of-the-art results across a wide range of tasks. By comparing to prior work, the authors show that their baselines match or surpass the performance of more sophisticated algorithms on 35 out of 47 datasets (almost 75%). The proposed standardized methodology for evaluation and baseline implementations aim to improve the rigour of empirical science in offline MARL. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Offline reinforcement learning is important because it can help robots and machines make good decisions without needing a lot of training data. Right now, it’s hard to compare different ideas because nobody agrees on how to measure which one works best. This paper figures out what the problems are and shows that simple computer programs (baselines) can do just as well as more complicated ones in many cases. The authors even provide their own versions of these baselines so that others can use them and make better comparisons. |
Keywords
» Artificial intelligence » Reinforcement learning
