MAexp: A Generic Platform for RL-based Multi-Agent Exploration
Shaohao Zhu,Jiacheng Zhou,Anjun Chen,Mingming Bai,Jiming Chen,Jinming Xu,Shaohao Zhu,Jiacheng Zhou,Anjun Chen,Mingming Bai,Jiming Chen,Jinming Xu
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 ...
