Efficient exploration is a well known problem in deep reinforcement learning and this problem is exacerbated in multi-agent reinforcement learning due the intrinsic complexities of such algorithms. There are several approaches to efficiently explore an environment to learn to solve tasks by multi-agent operating in that environment, of which, the idea of expert exploration is investigated in this work. More specifically, this work investigates the application of large-language models as expert planners for efficient exploration in planning based tasks for multiple agents.
@article{arxiv.2507.07302,
title = {Application of LLMs to Multi-Robot Path Planning and Task Allocation},
author = {Ashish Kumar},
journal= {arXiv preprint arXiv:2507.07302},
year = {2025}
}