Application of LLMs to Multi-Robot Path Planning and Task Allocation
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arXiv
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866916835609280512 |
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| author | Kumar, Ashish |
| author_facet | Kumar, Ashish |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_07302 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Application of LLMs to Multi-Robot Path Planning and Task Allocation Kumar, Ashish Artificial Intelligence Robotics 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. |
| title | Application of LLMs to Multi-Robot Path Planning and Task Allocation |
| topic | Artificial Intelligence Robotics |
| url | https://arxiv.org/abs/2507.07302 |