Text-adventure games and text role-playing games are grand challenges for reinforcement learning game playing agents. Text role-playing games are open-ended environments where an agent must faithfully play a particular character. We consider the distinction between characters and actors, where an actor agent has the ability to play multiple characters. We present a framework we call a thespian agent that can learn to emulate multiple characters along with a soft prompt that can be used to direct it as to which character to play at any time. We further describe an attention mechanism that allows the agent to learn new characters that are based on previously learned characters in a few-shot fashion. We show that our agent outperforms the state of the art agent framework in multi-character learning and few-shot learning.

翻译：文本冒险游戏和文本角色扮演游戏为强化学习游戏智能体带来了巨大挑战。文本角色扮演游戏是一种开放式环境，智能体必须忠实扮演特定角色。我们区分了角色与演员的概念，其中演员型智能体能够扮演多个角色。本文提出一种称为“剧作家智能体”的框架，该框架可学习模拟多个角色，并配备软提示机制，用于随时指示其应扮演的角色。我们进一步描述了一种注意力机制，使智能体能够基于先前习得的角色，通过少样本学习方式掌握新角色。实验表明，在多角色学习与少样本学习任务中，我们的智能体性能优于现有最先进的智能体框架。