Summary of Diffusion-based Episodes Augmentation For Offline Multi-agent Reinforcement Learning, by Jihwan Oh et al.
Diffusion-based Episodes Augmentation for Offline Multi-Agent Reinforcement Learning
by Jihwan Oh, Sungnyun Kim, Gahee Kim, Sunghwan Kim, Se-Young Yun
First submitted to arxiv on: 23 Aug 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 presents EAQ, a novel approach for offline multi-agent reinforcement learning (MARL) framework utilizing diffusion models. The authors integrate the Q-total function directly into the diffusion model to maximize global returns in an episode, eliminating the need for separate training. The focus is on cooperative scenarios where agents work together to achieve a shared goal. Experimental results show that EAQ significantly boosts offline MARL algorithm compared to the original dataset, improving normalized return by +17.3% and +12.9% for medium and poor behavioral policies in SMAC simulator. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Offline reinforcement learning is important because it allows agents to learn from past interactions without needing real-time data collection. This paper presents a new way to do offline multi-agent reinforcement learning using something called EAQ. EAQ uses diffusion models to help the agents work together better. The authors tested this approach and found that it works well, especially in cooperative scenarios where agents need to work together. |
Keywords
» Artificial intelligence » Diffusion model » Reinforcement learning
