论文标题
结构性相似性，以改善加固学习的转移
Structural Similarity for Improved Transfer in Reinforcement Learning
论文作者
论文摘要
转移学习是开发性能RL代理的越来越普遍的方法。但是，尚不清楚如何定义源和目标任务之间的关系，以及这种关系如何有助于成功转移。我们提出了一种称为两个MDP或SS2的结构相似性的算法，该算法基于先前开发的两次仿真度量来计算两个有限MDP的状态的状态相似性度量，并表明该量度满足距离度量的属性。然后，通过GRIDWORLD导航任务的经验结果，我们提供了证据表明，距离度量可用于改善Q学习代理的转移性能，而不是以前的实现。
Transfer learning is an increasingly common approach for developing performant RL agents. However, it is not well understood how to define the relationship between the source and target tasks, and how this relationship contributes to successful transfer. We present an algorithm called Structural Similarity for Two MDPS, or SS2, that calculates a state similarity measure for states in two finite MDPs based on previously developed bisimulation metrics, and show that the measure satisfies properties of a distance metric. Then, through empirical results with GridWorld navigation tasks, we provide evidence that the distance measure can be used to improve transfer performance for Q-Learning agents over previous implementations.
