MQE: Unleashing the Power of Interaction with Multi-agent Quadruped Environment Article Swipe

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Ziyan Xiong , Bo Chen , Shi‐Yu Huang , Wei-Wei Tu , Zhaofeng He , Yang Gao ·

YOU? · · 2024 · Open Access · · DOI: https://doi.org/10.48550/arxiv.2403.16015

The advent of deep reinforcement learning (DRL) has significantly advanced the field of robotics, particularly in the control and coordination of quadruped robots. However, the complexity of real-world tasks often necessitates the deployment of multi-robot systems capable of sophisticated interaction and collaboration. To address this need, we introduce the Multi-agent Quadruped Environment (MQE), a novel platform designed to facilitate the development and evaluation of multi-agent reinforcement learning (MARL) algorithms in realistic and dynamic scenarios. MQE emphasizes complex interactions between robots and objects, hierarchical policy structures, and challenging evaluation scenarios that reflect real-world applications. We present a series of collaborative and competitive tasks within MQE, ranging from simple coordination to complex adversarial interactions, and benchmark state-of-the-art MARL algorithms. Our findings indicate that hierarchical reinforcement learning can simplify task learning, but also highlight the need for advanced algorithms capable of handling the intricate dynamics of multi-agent interactions. MQE serves as a stepping stone towards bridging the gap between simulation and practical deployment, offering a rich environment for future research in multi-agent systems and robot learning. For open-sourced code and more details of MQE, please refer to https://ziyanx02.github.io/multiagent-quadruped-environment/ .

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Type: preprint
Language: en
Landing Page: http://arxiv.org/abs/2403.16015
PDF: https://arxiv.org/pdf/2403.16015
OA Status: green
Cited By: 1
Related Works: 10
OpenAlex ID: https://openalex.org/W4393212874

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DOI: https://doi.org/10.48550/arxiv.2403.16015

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Title: MQE: Unleashing the Power of Interaction with Multi-agent Quadruped Environment

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Type: preprint

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Language: en

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Publication year: 2024

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Publication date: 2024-03-24

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Authors: Ziyan Xiong, Bo Chen, Shi‐Yu Huang, Wei-Wei Tu, Zhaofeng He, Yang Gao

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Landing page: https://arxiv.org/abs/2403.16015

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Concepts: Power (physics), Computer science, Physics, Quantum mechanics

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Cited by: 1

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