论文标题
强化学习技术的调查：策略，最新发展和未来方向
A Survey of Reinforcement Learning Techniques: Strategies, Recent Development, and Future Directions
论文作者
论文摘要
强化学习是设计人工智能系统强调实时响应的核心组成部分之一。强化学习会影响系统在任意环境中采取行动是否具有对环境模型的知识。在本文中，我们介绍了一项有关强化学习的全面研究，重点关注各种维度，包括挑战，最近的最新技术的最新发展以及未来的方向。本文的基本目的是为展示可用的增强学习方法提供一个框架，这些方法足够且易于遵循该领域的新研究人员和学者，考虑到最新问题。首先，我们以易于理解和可比的方式说明了加固学习的核心技术。最后，我们分析并描述了加强学习方法的最新发展。我的分析指出，大多数模型都专注于调整策略价值观，而不是在特定的推理状态下调整其他事物。
Reinforcement learning is one of the core components in designing an artificial intelligent system emphasizing real-time response. Reinforcement learning influences the system to take actions within an arbitrary environment either having previous knowledge about the environment model or not. In this paper, we present a comprehensive study on Reinforcement Learning focusing on various dimensions including challenges, the recent development of different state-of-the-art techniques, and future directions. The fundamental objective of this paper is to provide a framework for the presentation of available methods of reinforcement learning that is informative enough and simple to follow for the new researchers and academics in this domain considering the latest concerns. First, we illustrated the core techniques of reinforcement learning in an easily understandable and comparable way. Finally, we analyzed and depicted the recent developments in reinforcement learning approaches. My analysis pointed out that most of the models focused on tuning policy values rather than tuning other things in a particular state of reasoning.
