One major challenge in reinforcement learning (RL) is the large amount of steps for the RL agent needs to converge in the training process and learn the optimal policy, especially in text-based game environments where the action space is extensive. However, non-player characters (NPCs) sometimes hold some key information about the game, which can potentially help to train RL agents faster. Thus, this paper explores how to interact and converse with NPC agents to get the key information using large language models (LLMs), as well as incorporate this information to speed up RL agent's training using knowledge graphs (KGs) and Story Shaping.

翻译：强化学习（RL）面临的一个主要挑战是训练过程中RL智能体需要大量步骤才能收敛并学习最优策略，尤其是在动作空间巨大的文本类游戏环境中。然而，非玩家角色（NPC）有时会掌握游戏的关键信息，这有可能帮助加速RL智能体的训练。因此，本文探讨了如何利用大型语言模型（LLMs）与NPC智能体进行交互和对话以获取关键信息，以及如何通过知识图谱（KGs）和故事塑造（Story Shaping）将这些信息整合进来，以加速RL智能体的训练。