We show that Reinforcement Learning (RL) methods for solving Text-Based Games
(TBGs) often fail to generalize on unseen games, especially in small data
regimes. To address this issue, we propose Context Relevant Episodic State
Truncation (CREST) for irrelevant token removal in observation text for
improved generalization. Our method first trains a base model using Q-learning,
which typically overfits the training games. The base model's action token
distribution is used to perform observation pruning that removes irrelevant
tokens. A second bootstrapped model is then retrained on the pruned observation
text. Our bootstrapped agent shows improved generalization in solving unseen
TextWorld games, using 10x-20x fewer training games compared to previous
state-of-the-art methods despite requiring less number of training episodes.

提出了一种基于 RL 的 CREST 方法，通过去除观测文本中的无关词汇来提高 RL 在 Text-Based Games 中的泛化性能，实验表明该方法可以使用更少的训练数据来解决新的游戏。