Summary of Coordination Failure in Cooperative Offline Marl, by Callum Rhys Tilbury et al.
Coordination Failure in Cooperative Offline MARL
by Callum Rhys Tilbury, Claude Formanek, Louise Beyers, Jonathan P. Shock, Arnu Pretorius
First submitted to arxiv on: 1 Jul 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 explores the challenges of offline multi-agent reinforcement learning (MARL) using static datasets and proposes a solution to mitigate coordination failure. It introduces the Best Response Under Data (BRUD) approach and identifies a previously overlooked failure mode that can lead to catastrophic results. The authors suggest prioritizing samples from the dataset based on joint-action similarity during policy learning, which is demonstrated to be effective in experiments. They also discuss the potential for combining this approach with other methods, such as critic and policy regularisation. Finally, they highlight the importance of drawing insights from simplified games that can be transferred to more complex contexts. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Offline MARL uses static data to learn optimal control. The paper focuses on coordination failure in the BRUD approach, which is a common method for offline MARL. It shows how ignoring joint-action similarity can lead to bad outcomes and proposes prioritizing similar actions during policy learning. This helps solve the problem of coordination failure. The authors also mention that this approach can be combined with other methods to make it more effective. |
Keywords
» Artificial intelligence » Reinforcement learning
