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Application of LLMs to Multi-Robot Path Planning and Task Allocation

arXiv 2507.07302 ↗First posted 9 Jul 2025cs.AI cs.ROcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 11 of its 32 references that are in the map. First posted on arXiv 9 Jul 2025.

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References · 11

  1. 2023Judging LLM-as-a-Judge with MT-Bench and Chatbot Arenacited by 8,626
  2. 2017Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environmentscited by 1,558
  3. 2018QMIX: Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learningcited by 708
  4. 2022PlanBench: An Extensible Benchmark for Evaluating Large Language Models on Planning and Reasoning about Changecited by 382
  5. 2017Emergence of Grounded Compositional Language in Multi-Agent Populationscited by 263
  6. 2018PRIMAL: Pathfinding via Reinforcement and Imitation Multi-Agent Learningcited by 90
  7. 2020PRIMAL2: Pathfinding via Reinforcement and Imitation Multi-Agent Learning -- Lifelongcited by 63
  8. 2021Exploration in Deep Reinforcement Learning: From Single-Agent to Multiagent Domaincited by 58
  9. 2023Enabling Intelligent Interactions between an Agent and an LLM: A Reinforcement Learning Approachcited by 28
  10. 2023TransfQMix: Transformers for Leveraging the Graph Structure of Multi-Agent Reinforcement Learning Problemscited by 9
  11. 2022Offline Robot Reinforcement Learning with Uncertainty-Guided Human Expert Samplingcited by 2

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  1. 2025Multi-Agent Systems for Robotic Autonomy with LLMs
  2. 2024Understanding the planning of LLM agents: A survey
  3. 2025A Survey on the Optimization of Large Language Model-based Agents
  4. 2024Trial and Error: Exploration-Based Trajectory Optimization for LLM Agents
  5. 2023SMART-LLM: Smart Multi-Agent Robot Task Planning using Large Language Models
  6. 2026Hierarchical LLM-Based Multi-Agent Framework with Prompt Optimization for Multi-Robot Task Planning
  7. 2025Large Language Models for Planning: A Comprehensive and Systematic Survey
  8. 2025Training Task Reasoning LLM Agents for Multi-turn Task Planning via Single-turn Reinforcement Learning