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.

通过利用自然语言处理技术和大型语言模型（LLMs）的Transformer，本文介绍了如何将深度强化学习(DRL)策略的部分转化为用户友好、类人的英文叙述，涉及到对策略模板的数学表达式进行解析、语义解释变量和结构、生成基于规则的主要解释，以及利用预训练的生成式Transformer（GPT）模型对解释进行改进和置于上下文。通过在一个示例中的后续定制和细致的验证过程，展示了这种方法的适用性和潜力。

面向可解释性策略模板的自然语言处理变换器