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

Bootstrapped Q-learning with Context Relevant Observation Pruning to Generalize in Text-based Games