Keep CALM and Explore: Language Models for Action Generation in Text-based Games
Shunyu Yao, Rohan Rao, Matthew Hausknecht, Karthik Narasimhan
Abstract
Text-based games present a unique challenge for autonomous agents to operate in natural language and handle enormous action spaces. In this paper, we propose the Contextual Action Language Model (CALM) to generate a compact set of action candidates at each game state. Our key insight is to train language models on human gameplay, where people demonstrate linguistic priors and a general game sense for promising actions conditioned on game history. We combine CALM with a reinforcement learning agent which re-ranks the generated action candidates to maximize in-game rewards. We evaluate our approach using the Jericho benchmark, on games unseen by CALM during training. Our method obtains a 69% relative improvement in average game score over the previous state-of-the-art model. Surprisingly, on half of these games, CALM is competitive with or better than other models that have access to ground truth admissible actions. Code and data are available at https://github.com/princeton-nlp/calm-textgame.- Anthology ID:
- 2020.emnlp-main.704
- 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:
- 8736–8754
- Language:
- URL:
- https://aclanthology.org/2020.emnlp-main.704
- DOI:
- 10.18653/v1/2020.emnlp-main.704
- Cite (ACL):
- Shunyu Yao, Rohan Rao, Matthew Hausknecht, and Karthik Narasimhan. 2020. Keep CALM and Explore: Language Models for Action Generation in Text-based Games. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 8736–8754, Online. Association for Computational Linguistics.
- Cite (Informal):
- Keep CALM and Explore: Language Models for Action Generation in Text-based Games (Yao et al., EMNLP 2020)
- PDF:
- https://preview.aclanthology.org/add_acl24_videos/2020.emnlp-main.704.pdf
- Code
- princeton-nlp/calm-textgame
- Data
- Jericho