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.

文本冒险游戏和文本角色扮演游戏是强化学习游戏代理面临的重大挑战。我们提出了一个称为 thespian agent 的框架，可以学习模仿多个角色，并使用软提示来指导其在任何时候扮演哪个角色。我们还描述了一种注意机制，使代理可以以少量数据学习基于先前学习角色的新角色。我们展示了我们的代理在多角色学习和少样本学习方面的性能优于现有的代理框架。

戏子：多角色文本角色扮演游戏代理

Thespian: Multi-Character Text Role-Playing Game Agents

We provide a dataset that enables the creation of learning agents that can
build knowledge graph-based world models of interactive narratives. Interactive
narratives -- or text-adventure games -- are partially observable environments
structured as long puzzles or quests in which an agent perceives and interacts
with the world purely through textual natural language. Each individual game
typically contains hundreds of locations, characters, and objects -- each with
their own unique descriptions -- providing an opportunity to study the problem
of giving language-based agents the structured memory necessary to operate in
such worlds. Our dataset provides 24198 mappings between rich natural language
observations and: (1) knowledge graphs that reflect the world state in the form
of a map; (2) natural language actions that are guaranteed to cause a change in
that particular world state. The training data is collected across 27 games in
multiple genres and contains a further 7836 heldout instances over 9 additional
games in the test set. We further provide baseline models using rules-based,
question-answering, and sequence learning approaches in addition to an analysis
of the data and corresponding learning tasks.

该研究提供了一个数据集，用于训练能够构建基于知识图谱的交互叙事世界模型的学习代理。数据集包含了 24198 个富自然语言观察和知识图谱之间的映射，以及多个流派的 27 个游戏中的训练数据和 9 个附加的游戏中包含的 7836 项测试集等内容。此外，研究还提供了基于规则、问答和序列学习方法的基线模型以及数据分析。