MAexp: A Generic Platform for RL-based Multi-Agent Exploration
This addresses the sim-to-real gap in RL-based multi-agent exploration for robotics, though it appears incremental as it builds on existing MARL methods with improvements in speed and flexibility.
The paper tackles the inefficiency and lack of diversity in multi-agent reinforcement learning (MARL) platforms for exploration by proposing MAexp, a generic platform that integrates various MARL algorithms and scenarios, achieving a sampling speed approximately 40 times faster than existing platforms.
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.