Multi-Agent Deep Reinforcement Learning for Multi-Robot Applications: A Survey

Abstract

Deep reinforcement learning has produced many success stories in recent years. Some example fields in which these successes have taken place include mathematics, games, health care, and robotics. In this paper, we are especially interested in multi-agent deep reinforcement learning, where multiple agents present in the environment not only learn from their own experiences but also from each other and its applications in multi-robot systems. In many real-world scenarios, one robot might not be enough to complete the given task on its own, and, therefore, we might need to deploy multiple robots who work together towards a common global objective of finishing the task. Although multi-agent deep reinforcement learning and its applications in multi-robot systems are of tremendous significance from theoretical and applied standpoints, the latest survey in this domain dates to 2004 albeit for traditional learning applications as deep reinforcement learning was not invented. We classify the reviewed papers in our survey primarily based on their multi-robot applications. Our survey also discusses a few challenges that the current research in this domain faces and provides a potential list of future applications involving multi-robot systems that can benefit from advances in multi-agent deep reinforcement learning.

Research on multi-robot collaborative operation in logistics and warehousing using A3C optimized YOLOv5-PPO model

“…The development of time series forecasting can be traced back many years, when statistical methods such as moving averages and exponential smoothing were widely used (Orr and Dutta, 2023 ). However, with the rise of machine learning and deep learning, traditional methods are gradually being replaced by more powerful models.…”

Metal–air batteries for powering robots

“…Autonomous mobile robots (AMRs) are a type of robot system designed to move payloads or perform specific tasks, requiring efficient, precise, and streamlined workflows. 154,155 With the advancement of sensor and information storage technologies, AMRs are being deployed in increasingly complex environments. Common AMRs include vacuum cleaners, lawn mowers, and logistics robots.…”

Probability-Based Strategy for a Football Multi-Agent Autonomous Robot System

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