论文标题
用常识性知识增强基于文本的增强学习者
Enhancing Text-based Reinforcement Learning Agents with Commonsense Knowledge
论文作者
论文摘要
在本文中，我们考虑了通过使用基于文本的环境和游戏作为评估环境来评估增强学习技术进展的最新趋势。对文本的这种依赖将自然语言处理的进步带入了这些代理的范围，反复出现的线程是使用外部知识来模仿和更好的人类水平的表现。我们提出了一种使用常识性知识的代理的实例化，以在两个基于文本的环境上显示出令人鼓舞的性能。
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.
