MAexp: A Generic Platform for RL-based Multi-Agent Exploration
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arXiv
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| Autori principali: | , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866907873148141568 |
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| author | Zhu, Shaohao Zhou, Jiacheng Chen, Anjun Bai, Mingming Chen, Jiming Xu, Jinming |
| author_facet | Zhu, Shaohao Zhou, Jiacheng Chen, Anjun Bai, Mingming Chen, Jiming Xu, Jinming |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2404_12824 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | MAexp: A Generic Platform for RL-based Multi-Agent Exploration Zhu, Shaohao Zhou, Jiacheng Chen, Anjun Bai, Mingming Chen, Jiming Xu, Jinming Robotics Machine Learning Multiagent Systems 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. |
| title | MAexp: A Generic Platform for RL-based Multi-Agent Exploration |
| topic | Robotics Machine Learning Multiagent Systems |
| url | https://arxiv.org/abs/2404.12824 |