论文标题
预先训练的单词嵌入，用于加强学习中的目标条件转移学习
Pre-trained Word Embeddings for Goal-conditional Transfer Learning in Reinforcement Learning
论文作者
论文摘要
强化学习（RL）算法通常启动Tabula Rasa，而没有任何先前的环境知识，并且没有任何先前的技能。但是，这通常会导致样品效率较低，需要与环境进行大量相互作用。在终生学习环境中尤其如此，在该环境中，代理需要不断扩展其功能。在本文中，我们研究了预先训练的任务独立语言模型如何使目标条件RL代理更有效。我们通过促进不同相关任务之间的转移学习来做到这一点。我们在一组对象导航任务上实验证明了我们的方法。
Reinforcement learning (RL) algorithms typically start tabula rasa, without any prior knowledge of the environment, and without any prior skills. This however often leads to low sample efficiency, requiring a large amount of interaction with the environment. This is especially true in a lifelong learning setting, in which the agent needs to continually extend its capabilities. In this paper, we examine how a pre-trained task-independent language model can make a goal-conditional RL agent more sample efficient. We do this by facilitating transfer learning between different related tasks. We experimentally demonstrate our approach on a set of object navigation tasks.
