In this paper, we consider the recent trend of evaluating progress on reinforcement learning technology by using text-based environments and games as evaluation environments. This reliance on text brings advances in natural language processing into the ambit of these agents, with a recurring thread being the use of external knowledge to mimic and better human-level performance. We present one such instantiation of agents that use commonsense knowledge from ConceptNet to show promising performance on two text-based environments.
@article{arxiv.2005.00811,
title = {Enhancing Text-based Reinforcement Learning Agents with Commonsense Knowledge},
author = {Keerthiram Murugesan and Mattia Atzeni and Pushkar Shukla and Mrinmaya Sachan and Pavan Kapanipathi and Kartik Talamadupula},
journal= {arXiv preprint arXiv:2005.00811},
year = {2020}
}