Exploration is an essential component of reinforcement learning algorithms, where agents need to learn how to predict and control unknown and often stochastic environments. Reinforcement learning agents depend crucially on exploration to obtain informative data for the learning process as the lack of enough information could hinder effective learning. In this article, we provide a survey of modern exploration methods in (Sequential) reinforcement learning, as well as a taxonomy of exploration methods.

翻译：探索是强化学习算法的一个基本组成部分,在这种算法中,代理商需要学会如何预测和控制未知和往往是随机的环境。强化学习代理商主要依靠探索,为学习过程获取信息数据,因为缺乏足够的信息会妨碍有效学习。在本条中,我们提供了对(渐进式)强化学习中现代探索方法的调查,以及勘探方法分类。