深度强化学习研究综述

作为一种崭新的机器学习方法,深度强化学习将深度学习和强化学习技术结合起来,使智能体能够从高维空间感知信息,并根据得到的信息训练模型、做出决策。由于深度强化学习算法具有通用性和有效性,人们对其进行了广泛的研究,并将其运用到了日常生活的各个领域。首先,对深度强化学习研究进行概述,介绍了深度强化学习的基础理论；然后,分别介绍了基于值函数和基于策略的深度强化学习算法,讨论了其应用前景；最后,对相关研究工作做了总结和展望。

Research on Deep Reinforcement Learning

As a new machine learning method,deep reinforcement learning combines deep learning and reinforcement learning,which makes that the agent can perceive the information from high dimensional space,train model and make decision according to the received information.Deep reinforcement learning has been widely researched and used in va-rious fields of daily life because of its universality and effectiveness.Firstly,an overview of the deep reinforcement lear-ning research was given and the basic theory of deep reinforcement learning was introduced.Then value-based algorithms and policy-based algorithms were introduced.After that,the application prospects of deep reinfercement learning were discussed.Finally,the related researches were summarized and prospected.