We consider the task of learning to play families of text-based computer adventure games, i.e., fully textual environments with a common theme (e.g. cooking) and goal (e.g. prepare a meal from a recipe) but with different specifics; new instances of such games are relatively straightforward for humans to master after a brief exposure to the genre but have been curiously difficult for computer agents to learn. We find that the deep Q-learning strategies that have been successfully leveraged for superhuman performance in single-instance action video games can be applied to learn families of text video games when adopting simple strategies that correlate with human-like learning behavior. Specifically, we build agents that learn to tackle simple scenarios before more complex ones (curriculum learning), that are equipped with the contextualized semantics of BERT (and we demonstrate that this provides a measure of common sense), and that familiarize themselves in an unfamiliar environment by navigating before acting. We demonstrate faster training convergence and improved task completion rates over reasonable baselines.

本研究探讨了如何应用深度强化学习策略来让计算机代理学会玩一类由文本组成的冒险游戏，并使用基于人类学习行为的简单策略如课程学习、导航探索和多臂赌博来提高游戏的任务完成率。

Learn How to Cook a New Recipe in a New House: Using Map
  Familiarization, Curriculum Learning, and Common Sense to Learn Families of
  Text-Based Adventure Games

利用地图熟悉、课程学习和赌徒反馈学习基于文本冒险游戏的系列