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首页> 外文会议>Conference on Artificial Intelligence and Robotics;Asia-Pacific International Symposium >Deep Reinforcement Learning Issues and Approaches for The Multi-Agent Centric Problems
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Deep Reinforcement Learning Issues and Approaches for The Multi-Agent Centric Problems

机译：多智能体中心问题的深度强化学习问题和方法

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摘要

Reinforcement learning is a subfield of machine learning which is similar to human learning. In recent years, it has drawn a considerable portion of researchers' attention and it has been revolutionized; such as its integration with deep learning. This integration has created a better understanding of the visual environments and end-to-end direct learning from pixels to solve problems that have previously been intractable. This improvement has led to the creation of various deep reinforcement learning algorithms with different goals. In this paper, deep reinforcement learning algorithms and their applications are reviewed and categorized. This work also addresses the advantages and disadvantages of algorithms and the challenges that are solved with appearance of deep reinforcement learning. In order to use these algorithms, there are important considerations that need to be addressed in each problem. These considerations are about the most important components of reinforcement learning, which has been analyzed and categorized as the important achievement of this paper.
机译：强化学习是机器学习的一个子领域，与人类学习类似。近年来，它吸引了相当一部分研究人员的注意力，并且进行了革新。例如其与深度学习的集成。这种集成使人们对视觉环境有了更好的理解，并可以从像素端到端直接学习以解决以前难以解决的问题。这种改进导致创建了具有不同目标的各种深度强化学习算法。本文对深度强化学习算法及其应用进行了综述和分类。这项工作还解决了算法的优缺点以及深度强化学习的出现所解决的挑战。为了使用这些算法，在每个问题中都需要考虑重要的考虑因素。这些注意事项是关于强化学习的最重要组成部分，已被分析和归类为本文的重要成果。

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