LOA: Logical Optimal Actions for Text-based Interaction Games
This work addresses the challenge of interpretability and performance in AI agents for natural language games, though it appears incremental as it builds on existing neuro-symbolic approaches.
The paper tackles the problem of action decision in text-based interaction games by introducing Logical Optimal Actions (LOA), a neuro-symbolic reinforcement learning architecture that combines neural networks with symbolic knowledge acquisition, and demonstrates it through a web-based platform with comparisons to other agents.
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. Code: https://github.com/ibm/loa