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

arXiv: https://arxiv.org/abs/2009.11896 · HTML: https://mlsanity.com/paper/2009.11896/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Sep 2020.

## Built on by (most cited first, 7 of 7)

- [Neuro-Symbolic Reinforcement Learning with First-Order Logic](https://mlsanity.com/paper/2110.10963/) (2021)
- [A Systematic Survey of Text Worlds as Embodied Natural Language Environments](https://mlsanity.com/paper/2107.04132/) (2021)
- [A Survey of Text Games for Reinforcement Learning informed by Natural Language](https://mlsanity.com/paper/2109.09478/) (2021)
- [LOA: Logical Optimal Actions for Text-based Interaction Games](https://mlsanity.com/paper/2110.10973/) (2021)
- [Eye of the Beholder: Improved Relation Generalization for Text-based Reinforcement Learning Agents](https://mlsanity.com/paper/2106.05387/) (2021)
- [Learning Symbolic Rules over Abstract Meaning Representations for Textual Reinforcement Learning](https://mlsanity.com/paper/2307.02689/) (2023)
- [ScriptWorld: Text Based Environment For Learning Procedural Knowledge](https://mlsanity.com/paper/2307.03906/) (2023)

## Abstract (arXiv)

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.