Thespian: Multi-Character Text Role-Playing Game Agents
Christopher Cui Xiangyu Peng Mark Riedl
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.