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.

中文翻译：

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强化学习技术综述：策略、近期发展和未来方向

强化学习是设计强调实时响应的人工智能系统的核心组件之一。强化学习会影响系统在任意环境中采取行动，无论之前是否了解环境模型。在本文中，我们对强化学习进行了全面的研究，重点关注各种维度，包括挑战、不同最先进技术的最新发展以及未来方向。本文的基本目标是提供一个框架，用于介绍可用的强化学习方法，该框架对于考虑到最新问题的该领域的新研究人员和学者来说，足够丰富且易于遵循。第一的，我们以一种易于理解和可比较的方式说明了强化学习的核心技术。最后，我们分析并描述了强化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值，而不是在特定推理状态下调整其他事物。