# LOA: Logical Optimal Actions for Text-based Interaction Games

arXiv: https://arxiv.org/abs/2110.10973 · HTML: https://mlsanity.com/paper/2110.10973/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 14 references that are in the map.
- First posted on arXiv 21 Oct 2021.

## Builds on (most cited first, 14 of 14)

- [TextWorld: A Learning Environment for Text-based Games](https://mlsanity.com/paper/1806.11532/) (2018)
- [Interactive Fiction Games: A Colossal Adventure](https://mlsanity.com/paper/1909.05398/) (2019)
- [Neural Logic Machines](https://mlsanity.com/paper/1904.11694/) (2019)
- [Logical Neural Networks](https://mlsanity.com/paper/2006.13155/) (2020)
- [Language Understanding for Text-based Games Using Deep Reinforcement Learning](https://mlsanity.com/paper/1506.08941/) (2015)
- [Playing Text-Adventure Games with Graph-Based Deep Reinforcement Learning](https://mlsanity.com/paper/1812.01628/) (2018)
- [Neural Logic Reinforcement Learning](https://mlsanity.com/paper/1904.10729/) (2019)
- [Learning Dynamic Belief Graphs to Generalize on Text-Based Games](https://mlsanity.com/paper/2002.09127/) (2020)
- [Text-based RL Agents with Commonsense Knowledge: New Challenges, Environments and Baselines](https://mlsanity.com/paper/2010.03790/) (2020)
- [Counting to Explore and Generalize in Text-based Games](https://mlsanity.com/paper/1806.11525/) (2018)
- [Internal Model from Observations for Reward Shaping](https://mlsanity.com/paper/1806.01267/) (2018)
- [Reinforcement Learning with External Knowledge by using Logical Neural Networks](https://mlsanity.com/paper/2103.02363/) (2021)
- [Bootstrapped Q-learning with Context Relevant Observation Pruning to Generalize in Text-based Games](https://mlsanity.com/paper/2009.11896/) (2020)
- [DAQN: Deep Auto-encoder and Q-Network](https://mlsanity.com/paper/1806.00630/) (2018)

## Built on by (most cited first, 6 of 6)

- [Neuro-Symbolic AI in 2024: A Systematic Review](https://mlsanity.com/paper/2501.05435/) (2025)
- [TextWorldExpress: Simulating Text Games at One Million Steps Per Second](https://mlsanity.com/paper/2208.01174/) (2022)
- [Behavior Cloned Transformers are Neurosymbolic Reasoners](https://mlsanity.com/paper/2210.07382/) (2022)
- [Neurosymbolic AI and its Taxonomy: a survey](https://mlsanity.com/paper/2305.08876/) (2023)
- [Commonsense Knowledge from Scene Graphs for Textual Environments](https://mlsanity.com/paper/2210.14162/) (2022)
- [DiffG-RL: Leveraging Difference between State and Common Sense](https://mlsanity.com/paper/2211.16002/) (2022)

## Abstract (arXiv)

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