A New Approach to Solving SMAC Task: Generating Decision Tree Code from Large Language Models

• URL: http://arxiv.org/abs/2410.16024v1
• Date: Mon, 21 Oct 2024 13:58:38 GMT
• Title: A New Approach to Solving SMAC Task: Generating Decision Tree Code from Large Language Models
• Authors: Yue Deng, Weiyu Ma, Yuxin Fan, Yin Zhang, Haifeng Zhang, Jian Zhao,
• Abstract summary: StarCraft Multi-Agent Challenge (SMAC) is one of the most commonly used experimental environments in multi-agent reinforcement learning (MARL) Traditional MARL algorithms often require interacting with the environment for up to 1 million steps to train a model. In this paper, we propose a novel approach to solving SMAC tasks called LLM-SMAC.
• Score: 8.457552813123597
• License:
• Abstract: StarCraft Multi-Agent Challenge (SMAC) is one of the most commonly used experimental environments in multi-agent reinforcement learning (MARL), where the specific task is to control a set number of allied units to defeat enemy forces. Traditional MARL algorithms often require interacting with the environment for up to 1 million steps to train a model, and the resulting policies are typically non-interpretable with weak transferability. In this paper, we propose a novel approach to solving SMAC tasks called LLM-SMAC. In our framework, agents leverage large language models (LLMs) to generate decision tree code by providing task descriptions. The model is further self-reflection using feedback from the rewards provided by the environment. We conduct experiments in the SMAC and demonstrate that our method can produce high-quality, interpretable decision trees with minimal environmental exploration. Moreover, these models exhibit strong transferability, successfully applying to similar SMAC environments without modification. We believe this approach offers a new direction for solving decision-making tasks in the future.

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