MAexp: A Generic Platform for RL-based Multi-Agent Exploration

• URL: http://arxiv.org/abs/2404.12824v1
• Date: Fri, 19 Apr 2024 12:00:10 GMT
• Title: MAexp: A Generic Platform for RL-based Multi-Agent Exploration
• Authors: Shaohao Zhu, Jiacheng Zhou, Anjun Chen, Mingming Bai, Jiming Chen, Jinming Xu,
• Abstract summary: Existing platforms suffer from the inefficiency in sampling and the lack of diversity in Multi-Agent Reinforcement Learning (MARL) algorithms. 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.
• Score: 5.672198570643586
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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