MAexp: A Generic Platform for RL-based Multi-Agent Exploration
<|reference_start|>MAexp: A Generic Platform for RL-based Multi-Agent Exploration: The sim-to-real gap poses a significant challenge in RL-based multi-agent exploration due to scene quantization and action discretization. Existing platforms suffer from the inefficiency in sampling and the lack of diversity in Multi-Agent Reinforcement Learning (MARL) algorithms across different scenarios, restraining their widespread applications. To fill these gaps, we propose MAexp, a generic platform for multi-agent exploration that integrates a broad range of state-of-the-art MARL algorithms and representative scenarios. Moreover, we employ point clouds to represent our exploration scenarios, leading to high-fidelity environment mapping and a sampling speed approximately 40 times faster than existing platforms. Furthermore, equipped with an attention-based Multi-Agent Target Generator and a Single-Agent Motion Planner, MAexp can work with arbitrary numbers of agents and accommodate various types of robots. Extensive experiments are conducted to establish the first benchmark featuring several high-performance MARL algorithms across typical scenarios for robots with continuous actions, which highlights the distinct strengths of each algorithm in different scenarios.<|reference_end|>
@article{zhu2024maexp:,
    title={MAexp: A Generic Platform for RL-based Multi-Agent Exploration},
    author={Shaohao Zhu, Jiacheng Zhou, Anjun Chen, Mingming Bai, Jiming Chen and
  Jinming Xu},
    journal={arXiv preprint arXiv:2404.12824},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.12824},
    primaryClass={cs.RO cs.LG cs.MA}
}