MQE: Unleashing the Power of Interaction with Multi-agent Quadruped Environment
<|reference_start|>MQE: Unleashing the Power of Interaction with Multi-agent Quadruped Environment: 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/ .<|reference_end|>
@article{xiong2024mqe:,
    title={MQE: Unleashing the Power of Interaction with Multi-agent Quadruped
  Environment},
    author={Ziyan Xiong, Bo Chen, Shiyu Huang, Wei-Wei Tu, Zhaofeng He, Yang Gao},
    journal={arXiv preprint arXiv:2403.16015},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.16015},
    primaryClass={cs.RO}
}