Deep Reinforcement Learning Issues and Approaches for The Multi-Agent Centric Problems

Abstract

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.