Enhancing Text-based Reinforcement Learning Agents with Commonsense Knowledge

Understand

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.