论文标题
关于符号问题的广义增强学习的关系抽象
Relational Abstractions for Generalized Reinforcement Learning on Symbolic Problems
论文作者
论文摘要
由于需要长时间的推理，因此在具有符号状态空间的问题中进行的加强学习具有挑战性。本文提出了一种新的方法，该方法利用了关系抽象的结合，并深入学习来学习有关此类问题的可推广Q功能。学习的Q功能可以有效地转移到具有不同对象名称和对象数量的相关问题，从而完全不同的状态空间。我们表明，无需明确的手工编码课程即可将学习的广义Q功能用于零射击转移到相关问题。对一系列问题的经验评估表明，我们的方法有助于将学习知识的有效零转移到包含许多对象的更大的问题实例。
Reinforcement learning in problems with symbolic state spaces is challenging due to the need for reasoning over long horizons. This paper presents a new approach that utilizes relational abstractions in conjunction with deep learning to learn a generalizable Q-function for such problems. The learned Q-function can be efficiently transferred to related problems that have different object names and object quantities, and thus, entirely different state spaces. We show that the learned generalized Q-function can be utilized for zero-shot transfer to related problems without an explicit, hand-coded curriculum. Empirical evaluations on a range of problems show that our method facilitates efficient zero-shot transfer of learned knowledge to much larger problem instances containing many objects.
