VMAS: A Vectorized Multi-Agent Simulator for Collective Robot Learning

Bettini, Matteo; Ryan, Kortvelesy,; Blumenkamp, Jan; Prorok, Amanda

doi:10.48550/arxiv.2207.03530

Cited by 1 publication

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“…VMAS: Vectorized Multi-Agent Simulator for Collective Robot Learning (VMAS) is an open-source software for multi-robot application benchmarking [ 258 ]. Some applications that are part of the software include swarm behaviors, such as flocking and dispersion, as well as object transportation and multi-robot football.…”

Section: Challenges and Discussionmentioning

confidence: 99%

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

Orr

Dutta

2023

Sensors

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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.

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Section: Challenges and Discussionmentioning

confidence: 99%