Learning Scalable Policies over Graphs for Multi-Robot Task Allocation using Capsule Attention Networks

Paul, Steve; Ghassemi, Payam; Chowdhury, Souma

doi:10.1109/icra46639.2022.9812370

Cited by 15 publications

(10 citation statements)

References 29 publications

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“…Their centralized training and decentralized execution method uses CNNs. Paul et al [ 129 ] proposed to use DRL for multi-robot task allocation. They proposed a neural network architecture that they called a Capsule Attention-based Mechanism, which contains a Graph Capsule Convolutional Neural Network (GCapCN) [ 244 ] and a Multi-head Attention mechanism (MHA) [ 245 , 246 ].…”

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

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

confidence: 99%

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

Orr

Dutta

2023

Sensors

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“…Learning‐based approaches often model the job allocation problem as a graph to apply the graph neural networks (GNN) for solving. GNNs are widely used to solve various combinatorial optimization problems in recent years 33 . The nodes and edges in the graph depict the relationship between the jobs and the robots 39 .…”

Section: Introductionmentioning

confidence: 99%

“…Wang and Gombolay 31 developed a graph attention network based RoboGNN scheduler for multi‐robot job allocation with deadline constraints utilizing imitation learning for training. Paul et al 33 constructed the capsule attention‐based mechanism model based on GNN for job allocation in a heterogeneous team with deadline constraints. The GNN has the over‐smoothing and neighborhood explosion challenges which are addressed in recent studies 40,41 …”

Section: Introductionmentioning

confidence: 99%

“…The decentralized approach involves allocating the jobs in a distributed manner. Market-based, 9,10,[26][27][28][29][30] learning-based [31][32][33] and threshold-based 12,[34][35][36][37] approaches fall under this category. In the market-based method, the members of the team bid for the jobs utilizing their resources in an auction mechanism.…”

mentioning

confidence: 99%

“…GNNs are widely used to solve various combinatorial optimization problems in recent years. 33 The nodes and edges in the graph depict the relationship between the jobs and the robots. 39 Gautier et al 32 proposed a deep Q-learning based distributed job allocation with experience relay in a homogeneous robot team.…”

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confidence: 99%

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Distributed job allocation using response threshold for heterogeneous robot team under deadline constraints

Vivek¹,

Kumari²

2023

Concurrency and Computation

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Summary Job allocation among the robots is a significant challenge in a multi‐robot environment. This article proposes a distributed algorithm for job allocation with deadline constraints. It works utilizing the response threshold model inspired by job distribution among insect societies in nature. The team divides the jobs among themselves based on their threshold capacity. The proposed approach focus on maximizing the total jobs completed with the available resources. The static case where the robots never fail is considered first for evaluating the algorithm. Then it is run in a dynamic environment with robot failure to study the effects on job allocation. Two experimental studies are conducted with varying amounts of resources. In static and dynamic cases under experimental study I, the jobs completion percentage increased by an average of 37.6% and 35.8%, respectively, over the existing method. Under experimental study II, the proposed algorithm completed all the jobs with improved resource utilization. The results show that the proposed algorithm achieves better jobs completion. The algorithm is then tested in a real‐world experimental environment using FireBird V mobile robots under static and dynamic cases. The outcomes indicate that the proposed approach is efficient in job allocation in practical circumstances.

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Temporal Task Graph Based Dynamic Agent Allocation for Applying Behavior Trees in Multi-agent Games

Li,

Zhang

et al. 2023

Communications in Computer and Information Science

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Learning Scalable Policies over Graphs for Multi-Robot Task Allocation using Capsule Attention Networks

Cited by 15 publications

References 29 publications

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

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

Distributed job allocation using response threshold for heterogeneous robot team under deadline constraints

Temporal Task Graph Based Dynamic Agent Allocation for Applying Behavior Trees in Multi-agent Games

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