Fire Burns, Sword Cuts: Commonsense Inductive Bias for Exploration in Text-based Games
Dongwon Ryu, Ehsan Shareghi, Meng Fang, Yunqiu Xu, Shirui Pan, Reza Haf
Abstract
Text-based games (TGs) are exciting testbeds for developing deep reinforcement learning techniques due to their partially observed environments and large action spaces. In these games, the agent learns to explore the environment via natural language interactions with the game simulator. A fundamental challenge in TGs is the efficient exploration of the large action space when the agent has not yet acquired enough knowledge about the environment. We propose CommExpl, an exploration technique that injects external commonsense knowledge, via a pretrained language model (LM), into the agent during training when the agent is the most uncertain about its next action. Our method exhibits improvement on the collected game scores during the training in four out of nine games from Jericho. Additionally, the produced trajectory of actions exhibit lower perplexity, when tested with a pretrained LM, indicating better closeness to human language.- Anthology ID:
- 2022.acl-short.56
- Volume:
- Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)
- Month:
- May
- Year:
- 2022
- Address:
- Dublin, Ireland
- Editors:
- Smaranda Muresan, Preslav Nakov, Aline Villavicencio
- Venue:
- ACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 515–522
- Language:
- URL:
- https://aclanthology.org/2022.acl-short.56
- DOI:
- 10.18653/v1/2022.acl-short.56
- Cite (ACL):
- Dongwon Ryu, Ehsan Shareghi, Meng Fang, Yunqiu Xu, Shirui Pan, and Reza Haf. 2022. Fire Burns, Sword Cuts: Commonsense Inductive Bias for Exploration in Text-based Games. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pages 515–522, Dublin, Ireland. Association for Computational Linguistics.
- Cite (Informal):
- Fire Burns, Sword Cuts: Commonsense Inductive Bias for Exploration in Text-based Games (Ryu et al., ACL 2022)
- PDF:
- https://preview.aclanthology.org/ingest-acl-2023-videos/2022.acl-short.56.pdf
- Code
- ktr0921/comm-expl-kg-a2c
- Data
- Jericho