A Multi-Agent Reinforcement Learning Framework for Intelligent Manufacturing With Autonomous Mobile Robots

Agrawal, Akash; Won, Sung Jun; Sharma, Tushar; Deshpande, Mayuri; McComb, Christopher

doi:10.1017/pds.2021.17

Cited by 15 publications

(10 citation statements)

References 33 publications

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“…Policy Gradients DDPG PPO Other [23,24,39,40,59, (QMIX), [38] (DDQN), [93] (DDQN), [94] (DDQN), [95] (DQN), [96] (DQN) [46][47][48][49][50][97][98][99][100][101][102][103][104][105][106], [22,[52][53][54][55][56][57] [86,88,129-140] (TRPO), [141] (TRPO), [81] (TRPO), [142] (TD3), [143] (SAC), [144] (SAC)…”

Section: Q-networkmentioning

confidence: 99%

“…Ground Robots Manipulators [23,24,38,46,53,56,57,68,76,83,85,91,104,106,110,110,120,123,134,135,138,139,141,142,161,167,171,172,186,205,207,[218][219][220]230,240,269] [ 22,39,49,52,54,55,59,70,71,[73][74][75][77][78][79]…”

Section: Aerial Robotsmentioning

confidence: 99%

“…The robots learn to coordinate their actions in the assembly line. Agrawal et al [ 52 ] performed a case study on a DRL approach to handling a homogeneous multi-robot system that can communicate while operating in an industry setting. PPO is used as the foundation algorithm.…”

Section: Multi-robot System Applications Of Multi-agent Deep Reinforc...mentioning

confidence: 99%

“…Another popular algorithm in the multi-robot domain is PPO, potentially because of its relatively simple implementation [ 43 ]. PPO-clip and PPO-penalty are its two primary variants that are used in robotics [ 52 , 53 , 54 , 55 , 56 , 57 ].…”

Section: Introductionmentioning

confidence: 99%

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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: Q-networkmentioning

confidence: 99%

Section: Aerial Robotsmentioning

confidence: 99%

Section: Multi-robot System Applications Of Multi-agent Deep Reinforc...mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Orr

Dutta

2023

Sensors

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“…MAS are a class of AI systems that employs a number of intelligent agents working together to accomplish a single objective [8]. Each agent is a software entity with the ability to perceive its surroundings, think through its actions, and interact with other agents to plan out their actions [9].…”

Section: Multi-agent Systemsmentioning

confidence: 99%

Intelligent Systems in Healthcare: An Architecture Proposal

Chaves,

Montenegro,

Peixoto

et al. 2023

Lecture Notes in Networks and Systems

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Multi-Agent Systems has existed for decades and has focused on principles such as loose coupling, distribution, reactivity, and local state. Despite substantial tool and programming language research and development, industry adoption of these systems has been restricted, particularly in the healthcare arena. Artificial intelligence, on the other hand, entails developing computer systems that can execute tasks that normally require human intelligence, such as decision-making, problemsolving, and learning. The goal of this article is to develop and implement an architecture that includes multi-agent systems with microservices, leveraging the capabilities of both methodologies in order to harness the power of Artificial Intelligence in the healthcare industry. It assesses the proposed architecture's merits and downsides, as well as its relevance to various healthcare use cases and the influence on system scalability, adaptability, and maintainability. Indeed, the proposed architecture is capable of meeting the objectives while maintaining scalability, flexibility, and adaptability.

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