Multi-agent reinforcement learning is a key method for training multi-robot systems over a series of episodes in which robots are rewarded or punished according to their performance; only once the system is trained to a suitable standard is it deployed in the real world. If the system is not trained enough, the task will likely not be completed and could pose a risk to the surrounding environment. We introduce Multi-Agent Reinforcement Learning guided by Language-based Inter-Robot Negotiation (MARLIN), in which the training process requires fewer training episodes to reach peak performance. Robots are equipped with large language models that negotiate and debate a task, producing plans used to guide the policy during training. The approach dynamically switches between using reinforcement learning and large language model-based action negotiation throughout training. This reduces the number of training episodes required, compared to standard multi-agent reinforcement learning, and hence allows the system to be deployed to physical hardware earlier. The performance of this approach is evaluated against multi-agent reinforcement learning, showing that our hybrid method achieves comparable results with significantly reduced training time.
View on arXiv@article{godfrey2025_2410.14383,
title={ MARLIN: Multi-Agent Reinforcement Learning Guided by Language-Based Inter-Robot Negotiation },
author={ Toby Godfrey and William Hunt and Mohammad D. Soorati },
journal={arXiv preprint arXiv:2410.14383},
year={ 2025 }
}