%0 Conference Proceedings %T LOA: Logical Optimal Actions for Text-based Interaction Games %A Kimura, Daiki %A Chaudhury, Subhajit %A Ono, Masaki %A Tatsubori, Michiaki %A Agravante, Don Joven %A Munawar, Asim %A Wachi, Akifumi %A Kohita, Ryosuke %A Gray, Alexander %Y Ji, Heng %Y Park, Jong C. %Y Xia, Rui %S Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing: System Demonstrations %D 2021 %8 August %I Association for Computational Linguistics %C Online %F kimura-etal-2021-loa %X We present Logical Optimal Actions (LOA), an action decision architecture of reinforcement learning applications with a neuro-symbolic framework which is a combination of neural network and symbolic knowledge acquisition approach for natural language interaction games. The demonstration for LOA experiments consists of a web-based interactive platform for text-based games and visualization for acquired knowledge for improving interpretability for trained rules. This demonstration also provides a comparison module with other neuro-symbolic approaches as well as non-symbolic state-of-the-art agent models on the same text-based games. Our LOA also provides open-sourced implementation in Python for the reinforcement learning environment to facilitate an experiment for studying neuro-symbolic agents. Demo site: https://ibm.biz/acl21-loa, Code: https://github.com/ibm/loa %R 10.18653/v1/2021.acl-demo.27 %U https://aclanthology.org/2021.acl-demo.27 %U https://doi.org/10.18653/v1/2021.acl-demo.27 %P 227-231