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A Novel Step towards Deep-Reinforcement Learning in a Cooperative Multi-agent System

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Document pages: 9 pages

Abstract: This study is an attempt to present a brief survey of several excellent works done by various authors in the field of multi-agent learning as well as multi-agent deep learning towards improving coordination and learning efficiency in the same. Based on the review of the existing work and research findings, we have proposed a framework to address coordination and learning issues in multi-agent learning. In this paper, we present a Networked–Deep Multi-agent Learning framework (N-DMAL), based upon implementation of deep reinforcement learning with social networks, that will result in improved learning efficiency of agents while interacting in a networked multi-agent system. The presented approach extends the traditional deep reinforcementlearning algorithm for agents’ interaction with other neighbouring agents when they coordinate in a cooperative manner.

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