Summary of Smac-r1: the Emergence Of Intelligence in Decision-making Tasks, by Yue Deng et al.
SMAC-R1: The Emergence of Intelligence in Decision-Making Tasks
by Yue Deng, Weiyu Ma, Yuxin Fan, Ruyi Song, Yin Zhang, Haifeng Zhang, Jian Zhao
First submitted to arxiv on: 21 Oct 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- 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 introduces SMAC-R1, a novel approach to multi-agent reinforcement learning (MARL) that leverages large language models (LLMs) to generate interpretable decision trees. By distilling knowledge from DeepSeek-Coder-v2.5-236B, the proposed method, Qwen2.5-7B-Base LLM, is capable of producing high-quality policies with minimal environmental exploration. The approach involves using feedback from rewards provided by the environment to fine-tune a small LLM and augment generated scripts through Supervised Fine-Tuning (SFT) and Group Relative Policy Optimization (GRPO). Experimental results on 23 original SMAC tasks and 10 newly-designed tasks demonstrate the effectiveness of this method, achieving strong transferability without modification. The authors believe that this approach offers a new direction for solving decision-making tasks and domain-specific LLM training pipelines in the future. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper introduces a new way to solve multi-agent problems by using big language models. It’s like having a super smart friend who can help you make decisions quickly and accurately. The method uses a special kind of AI called a large language model (LLM) to generate decision trees that are easy to understand. This helps agents learn from experience without needing to explore the environment as much. The authors tested their approach on 33 different tasks and found it worked really well, even when applying it to new situations. They think this could be an important step in developing AI that can make good decisions. |
Keywords
» Artificial intelligence » Fine tuning » Large language model » Optimization » Reinforcement learning » Supervised » Transferability
