This paper bridges the gap between mathematical heuristic strategies learned from Deep Reinforcement Learning (DRL) in automated agent negotiation, and comprehensible, natural language explanations. Our aim is to make these strategies more accessible to non-experts. By leveraging traditional Natural Language Processing (NLP) techniques and Large Language Models (LLMs) equipped with Transformers, we outline how parts of DRL strategies composed of parts within strategy templates can be transformed into user-friendly, human-like English narratives. To achieve this, we present a top-level algorithm that involves parsing mathematical expressions of strategy templates, semantically interpreting variables and structures, generating rule-based primary explanations, and utilizing a Generative Pre-trained Transformer (GPT) model to refine and contextualize these explanations. Subsequent customization for varied audiences and meticulous validation processes in an example illustrate the applicability and potential of this approach.

翻译：本文旨在弥合自主智能体谈判中通过深度强化学习(DRL)习得的数学启发式策略与可理解的自然语言解释之间的鸿沟。研究目标在于使非专业用户更易理解此类策略。通过融合传统自然语言处理(NLP)技术与配备Transformer架构的大语言模型(LLMs)，本文阐述了如何将策略模板中由DRL策略组件构成的数学元素转化为用户友好型类人英语叙事。为此，我们提出一种顶层算法，该算法包含策略模板数学表达式的解析、变量与结构的语义解释、基于规则的初级解释生成，以及利用生成式预训练Transformer(GPT)模型对解释进行精炼与语境化处理。后续面向不同受众的定制化过程及严格的验证案例，充分展现了该方法的适用性与潜力。