Abstract
Deep reinforcement learning provides a promising approach for text-based games in studying natural language communication between humans and artificial agents. However, the generalization still remains a big challenge as the agents depend critically on the complexity and variety of training tasks. In this paper, we address this problem by introducing a hierarchical framework built upon the knowledge graph-based RL agent. In the high level, a meta-policy is executed to decompose the whole game into a set of subtasks specified by textual goals, and select one of them based on the KG. Then a sub-policy in the low level is executed to conduct goal-conditioned reinforcement learning. We carry out experiments on games with various difficulty levels and show that the proposed method enjoys favorable generalizability.- Anthology ID:
- 2021.findings-emnlp.116
- Volume:
- Findings of the Association for Computational Linguistics: EMNLP 2021
- Month:
- November
- Year:
- 2021
- Address:
- Punta Cana, Dominican Republic
- Venue:
- Findings
- SIG:
- SIGDAT
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 1343–1353
- Language:
- URL:
- https://aclanthology.org/2021.findings-emnlp.116
- DOI:
- 10.18653/v1/2021.findings-emnlp.116
- Cite (ACL):
- Yunqiu Xu, Meng Fang, Ling Chen, Yali Du, and Chengqi Zhang. 2021. Generalization in Text-based Games via Hierarchical Reinforcement Learning. In Findings of the Association for Computational Linguistics: EMNLP 2021, pages 1343–1353, Punta Cana, Dominican Republic. Association for Computational Linguistics.
- Cite (Informal):
- Generalization in Text-based Games via Hierarchical Reinforcement Learning (Xu et al., Findings 2021)
- PDF:
- https://preview.aclanthology.org/emnlp-22-attachments/2021.findings-emnlp.116.pdf
- Code
- yunqiuxu/h-kga