Dialogue Shaping: Empowering Agents through NPC Interaction
Wei Zhou Xiangyu Peng Mark Riedl
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.