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.

我们提出了一个通用的多 Agent 探索平台 MAexp，该平台整合了各种最先进的 MARL 算法和代表性场景，通过使用点云来表示探索场景，实现了高保真度的环境映射和大约比现有平台快 40 倍的采样速度。此外，MAexp 还配备了基于注意力的多 Agent 目标生成器和单 Agent 运动规划器，可以适应任意数量的 Agent 和各种类型的机器人。我们进行了大量实验，建立了一个旨在为具有连续动作的机器人在典型场景下展示几种高性能 MARL 算法的第一个基准。