Multi-agent robotic systems are prone to deadlocks in an obstacle environment
where the system can get stuck away from its desired location under a smooth
low-level control policy. Without an external intervention, often in terms of a
high-level command, it is not possible to guarantee that just a low-level
control policy can resolve such deadlocks. Utilizing the generalizability and
low data requirements of large language models (LLMs), this paper explores the
possibility of using LLMs for deadlock resolution. We propose a hierarchical
control framework where an LLM resolves deadlocks by assigning a leader and
direction for the leader to move along. A graph neural network (GNN) based
low-level distributed control policy executes the assigned plan. We
systematically study various prompting techniques to improve LLM's performance
in resolving deadlocks. In particular, as part of prompt engineering, we
provide in-context examples for LLMs. We conducted extensive experiments on
various multi-robot environments with up to 15 agents and 40 obstacles. Our
results demonstrate that LLM-based high-level planners are effective in
resolving deadlocks in MRS.

利用大型语言模型 (LLMs) 和分层控制框架，在多智能体机器人系统中解决死锁问题，通过分配领导者和移动方向的方式，以 LLMs 为基础的高层规划帮助解决 MRS 中的死锁问题。

大型语言模型拯救行动：多机器人系统的死锁解决

Large Language Models to the Rescue: Deadlock Resolution in Multi-Robot  Systems

Multi-agent robotic systems are increasingly operating in real-world
environments in close proximity to humans, yet are largely controlled by policy
models with inscrutable deep neural network representations. We introduce a
method for incorporating interpretable concepts from a domain expert into
models trained through multi-agent reinforcement learning, by requiring the
model to first predict such concepts then utilize them for decision making.
This allows an expert to both reason about the resulting concept policy models
in terms of these high-level concepts at run-time, as well as intervene and
correct mispredictions to improve performance. We show that this yields
improved interpretability and training stability, with benefits to policy
performance and sample efficiency in a simulated and real-world
cooperative-competitive multi-agent game.

本文介绍了一种将领域专家的可解释概念纳入到多智能体强化学习模型中的方法，以提高模型的解释性和稳定性，提高性能和样本效率。