Abstract
We present lilGym, a new benchmark for language-conditioned reinforcement learning in visual environments. lilGym is based on 2,661 highly-compositional human-written natural language statements grounded in an interactive visual environment. We introduce a new approach for exact reward computation in every possible world state by annotating all statements with executable Python programs. Each statement is paired with multiple start states and reward functions to form thousands of distinct Markov Decision Processes of varying difficulty. We experiment with lilGym with different models and learning regimes. Our results and analysis show that while existing methods are able to achieve non-trivial performance, lilGym forms a challenging open problem. lilGym is available at https://lil.nlp.cornell.edu/lilgym/.- Anthology ID:
- 2023.acl-long.512
- Volume:
- Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
- Month:
- July
- Year:
- 2023
- Address:
- Toronto, Canada
- Editors:
- Anna Rogers, Jordan Boyd-Graber, Naoaki Okazaki
- Venue:
- ACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 9214–9234
- Language:
- URL:
- https://aclanthology.org/2023.acl-long.512
- DOI:
- 10.18653/v1/2023.acl-long.512
- Cite (ACL):
- Anne Wu, Kiante Brantley, Noriyuki Kojima, and Yoav Artzi. 2023. lilGym: Natural Language Visual Reasoning with Reinforcement Learning. In Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 9214–9234, Toronto, Canada. Association for Computational Linguistics.
- Cite (Informal):
- lilGym: Natural Language Visual Reasoning with Reinforcement Learning (Wu et al., ACL 2023)
- PDF:
- https://preview.aclanthology.org/ingest-acl-2023-videos/2023.acl-long.512.pdf