论文标题
Deep RL的自动课程学习：一项简短的调查
Automatic Curriculum Learning For Deep RL: A Short Survey
论文作者
论文摘要
自动课程学习（ACL）已成为深度强化学习（DRL）最近成功的基石。这些方法通过挑战其适应能力的任务来塑造代理的学习轨迹。近年来，它们已被用来提高样本效率和渐近性能，组织探索，鼓励概括或解决稀疏的奖励问题等。这项工作的野心是双重的：1）提出对自动课程学习文献的紧凑而易于访问的介绍，以及2）为ACL中当前的最新状态画出更大的了解，以鼓励对现有概念的交叉繁殖和新想法的出现。
Automatic Curriculum Learning (ACL) has become a cornerstone of recent successes in Deep Reinforcement Learning (DRL).These methods shape the learning trajectories of agents by challenging them with tasks adapted to their capacities. In recent years, they have been used to improve sample efficiency and asymptotic performance, to organize exploration, to encourage generalization or to solve sparse reward problems, among others. The ambition of this work is dual: 1) to present a compact and accessible introduction to the Automatic Curriculum Learning literature and 2) to draw a bigger picture of the current state of the art in ACL to encourage the cross-breeding of existing concepts and the emergence of new ideas.
