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Enhancing Text-based Reinforcement Learning Agents with Commonsense Knowledge
[article]
2020
arXiv
pre-print
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.
arXiv:2005.00811v1
fatcat:4c2i7k72gjduxczadwyefdon3m