Summary of Flickerfusion: Intra-trajectory Domain Generalizing Multi-agent Rl, by Woosung Koh et al.
FlickerFusion: Intra-trajectory Domain Generalizing Multi-Agent RL
by Woosung Koh, Wonbeen Oh, Siyeol Kim, Suhin Shin, Hyeongjin Kim, Jaein Jang, Junghyun Lee, Se-Young Yun
First submitted to arxiv on: 21 Oct 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 This paper addresses the challenge of multi-agent reinforcement learning (MARL) in complex cooperative tasks, where entities are dynamically removed or added during inference. Current MARL approaches assume a constant number of entities, which overlooks scenarios common in real-world applications like search and rescue missions and dynamic combat situations. The authors propose FlickerFusion, a novel method that stochastically drops out parts of the observation space to emulate being in-domain when inferenced out-of-domain. The results show superior inference rewards and reduced uncertainty compared to existing methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In this paper, scientists try to solve a big problem in artificial intelligence. They’re trying to figure out how to make computers work well together in situations where some of the things they need to pay attention to might change while they’re working. This is important because it could help computers do tasks that are hard for them now, like helping people in search and rescue missions or fighting wars. The scientists created a new way to make this work called FlickerFusion, which helps computers be more accurate and confident even when things change unexpectedly. |
Keywords
» Artificial intelligence » Attention » Inference » Reinforcement learning
