用大模型反馈自动设计技能奖励，实现自然语言驱动的智能体构建
MaestroMotif: Skill Design from Artificial Intelligence Feedback
- 基于自然语言描述，用大模型生成对应技能的奖励函数
- 结合强化学习与代码生成，训练出高性能可组合的智能体技能
- 在NetHack环境中表现优于现有方法，适合需要快速原型设计的研究者
用自然语言描述技能具有将人类决策知识注入AI系统的潜力。我们提出MaestroMotif，一种人工智能辅助的技能设计方法，能够生成高性能且可适应的智能体。MaestroMotif利用大语言模型（LLM）的能力，高效地创建和复用技能：首先，基于技能的自然语言描述，由LLM自动生成对应的奖励函数；随后，结合LLM的代码生成能力与强化学习，训练技能并组合实现语言指定的复杂行为。我们在NetHack学习环境（NLE）中的一系列复杂任务上评估了MaestroMotif，结果表明其在性能和易用性方面均优于现有方法。
原文摘要 · Abstract (English)
Describing skills in natural language has the potential to provide an accessible way to inject human knowledge about decision-making into an AI system. We present MaestroMotif, a method for AI-assisted skill design, which yields high-performing and adaptable agents. MaestroMotif leverages the capabilities of Large Language Models (LLMs) to effectively create and reuse skills. It first uses an LLM's feedback to automatically design rewards corresponding to each skill, starting from their natural language description. Then, it employs an LLM's code generation abilities, together with reinforcement learning, for training the skills and combining them to implement complex behaviors specified in language. We evaluate MaestroMotif using a suite of complex tasks in the NetHack Learning Environment (NLE), demonstrating that it surpasses existing approaches in both performance and usability.
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