AI agents powered by Large Language Models (LLMs) have made significant advances, enabling them to assist humans in diverse complex tasks and leading to a revolution in human-AI coordination.
A crucial limitation of this framework is that every policy in the pool is optimized w. r. t.
Simply waiting for every robot being ready for the next action can be particularly time-inefficient.
In this paper, we extend the state-of-the-art single-agent visual navigation method, Active Neural SLAM (ANS), to the multi-agent setting by introducing a novel RL-based planning module, Multi-agent Spatial Planner (MSP). MSP leverages a transformer-based architecture, Spatial-TeamFormer, which effectively captures spatial relations and intra-agent interactions via hierarchical spatial self-attentions.
This is often due to the belief that PPO is significantly less sample efficient than off-policy methods in multi-agent systems.