Summary of Hybrid Training For Enhanced Multi-task Generalization in Multi-agent Reinforcement Learning, by Mingliang Zhang et al.
Hybrid Training for Enhanced Multi-task Generalization in Multi-agent Reinforcement Learning
by Mingliang Zhang, Sichang Su, Chengyang He, Guillaume Sartoretti
First submitted to arxiv on: 24 Aug 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: 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 introduces HyGen, a hybrid multi-agent reinforcement learning (MARL) framework that combines online and offline learning to achieve both multi-task generalization and training efficiency. The framework extracts potential general skills from offline datasets and trains policies to select optimal skills under a centralized training and decentralized execution paradigm (CTDE). It utilizes a replay buffer integrating offline data and online interactions, demonstrating impressive generalization to unseen tasks on the StarCraft challenge. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary HyGen is a new way to teach artificial intelligence to do many things at once. Right now, AI can only learn one thing at a time, which means it’s not very good at doing lots of different things. The researchers created a special system that combines two ways of teaching AI: online learning and offline learning. They tested this system on a game called StarCraft and found that it worked really well. This could be useful in real life because it would allow AI to learn many skills at once, making it more helpful. |
Keywords
» Artificial intelligence » Generalization » Multi task » Online learning » Reinforcement learning
