深度强化学习简介

Vincent François-Lavet, Peter Henderson, Riashat Islam, Marc G. Bellemare, Joelle Pineau
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引用次数: 875

摘要

深度强化学习是强化学习(RL)和深度学习的结合。这一研究领域已经能够解决大量复杂的决策任务,而这些任务以前是机器无法完成的。因此,深度强化学习在医疗保健、机器人、智能电网、金融等领域开辟了许多新的应用。本文介绍了深度强化学习模型、算法和技术。特别关注与泛化相关的方面以及深度强化学习如何用于实际应用。我们假设读者熟悉基本的机器学习概念。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
An Introduction to Deep Reinforcement Learning
Deep reinforcement learning is the combination of reinforcement learning (RL) and deep learning. This field of research has been able to solve a wide range of complex decision-making tasks that were previously out of reach for a machine. Thus, deep RL opens up many new applications in domains such as healthcare, robotics, smart grids, finance, and many more. This manuscript provides an introduction to deep reinforcement learning models, algorithms and techniques. Particular focus is on the aspects related to generalization and how deep RL can be used for practical applications. We assume the reader is familiar with basic machine learning concepts.
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