论文标题
深入强化学习中无监督的表示学习：评论
Unsupervised Representation Learning in Deep Reinforcement Learning: A Review
论文作者
论文摘要
这篇综述解决了在深度强化学习（DRL）背景下学习测量数据的抽象表示的问题。尽管数据通常是模棱两可，高维且复杂的解释，但许多动态系统可以通过一组低维状态变量有效地描述。从数据中发现这些状态变量是（i）提高DRL方法的数据效率，鲁棒性和概括，（ii）应对维度的诅咒，以及（iii）将可解释性和见解带入Black-Box DRL的关键方面。这篇综述通过描述用于学习世界的学习代表的主要深度学习工具，对方法和原则进行系统的看法，总结应用程序，基准测试和评估策略，并讨论开放挑战和未来方向，从而对DRL的无监督表示学习进行了全面而完整的概述。
This review addresses the problem of learning abstract representations of the measurement data in the context of Deep Reinforcement Learning (DRL). While the data are often ambiguous, high-dimensional, and complex to interpret, many dynamical systems can be effectively described by a low-dimensional set of state variables. Discovering these state variables from the data is a crucial aspect for (i) improving the data efficiency, robustness, and generalization of DRL methods, (ii) tackling the curse of dimensionality, and (iii) bringing interpretability and insights into black-box DRL. This review provides a comprehensive and complete overview of unsupervised representation learning in DRL by describing the main Deep Learning tools used for learning representations of the world, providing a systematic view of the method and principles, summarizing applications, benchmarks and evaluation strategies, and discussing open challenges and future directions.
