Abstract
Using natural language as a hint can supply an additional reward for playing sparse-reward games. Achieving a goal should involve several different hints, while the given hints are usually incomplete. Those unmentioned latent hints still rely on the sparse reward signal, and make the learning process difficult. In this paper, we propose semi-supervised initialization (SSI) that allows the agent to learn from various possible hints before training under different tasks. Experiments show that SSI not only helps to learn faster (1.2x) but also has a higher success rate (11% relative improvement) of the final policy.- Anthology ID:
- 2021.naacl-main.249
- Volume:
- Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
- Month:
- June
- Year:
- 2021
- Address:
- Online
- Editors:
- Kristina Toutanova, Anna Rumshisky, Luke Zettlemoyer, Dilek Hakkani-Tur, Iz Beltagy, Steven Bethard, Ryan Cotterell, Tanmoy Chakraborty, Yichao Zhou
- Venue:
- NAACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 3112–3116
- Language:
- URL:
- https://aclanthology.org/2021.naacl-main.249
- DOI:
- 10.18653/v1/2021.naacl-main.249
- Cite (ACL):
- Tsu-Jui Fu and William Yang Wang. 2021. Semi-Supervised Policy Initialization for Playing Games with Language Hints. In Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 3112–3116, Online. Association for Computational Linguistics.
- Cite (Informal):
- Semi-Supervised Policy Initialization for Playing Games with Language Hints (Fu & Wang, NAACL 2021)
- PDF:
- https://preview.aclanthology.org/add_acl24_videos/2021.naacl-main.249.pdf
- Code
- tsujuifu/code_ssi