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

Subhajit Chaudhury, Daiki Kimura, Kartik Talamadupula, Michiaki Tatsubori, Asim Munawar, Ryuki Tachibana


Abstract
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 (SOTA) methods despite requiring fewer number of training episodes.
Anthology ID:
2020.emnlp-main.241
Volume:
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)
Month:
November
Year:
2020
Address:
Online
Editors:
Bonnie Webber, Trevor Cohn, Yulan He, Yang Liu
Venue:
EMNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
3002–3008
Language:
URL:
https://aclanthology.org/2020.emnlp-main.241
DOI:
10.18653/v1/2020.emnlp-main.241
Bibkey:
Cite (ACL):
Subhajit Chaudhury, Daiki Kimura, Kartik Talamadupula, Michiaki Tatsubori, Asim Munawar, and Ryuki Tachibana. 2020. Bootstrapped Q-learning with Context Relevant Observation Pruning to Generalize in Text-based Games. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 3002–3008, Online. Association for Computational Linguistics.
Cite (Informal):
Bootstrapped Q-learning with Context Relevant Observation Pruning to Generalize in Text-based Games (Chaudhury et al., EMNLP 2020)
Copy Citation:
PDF:
https://preview.aclanthology.org/nschneid-patch-1/2020.emnlp-main.241.pdf
Video:
 https://slideslive.com/38938770
Code
 IBM/context-relevant-pruning-textrl