LLM-Mediated Guidance of MARL Systems
Authors: name: Philipp D. Siedler, name: Ian Gemp

In complex multi-agent environments, achieving efficient learning and
desirable behaviours is a significant challenge for Multi-Agent Reinforcement
Learning (MARL) systems. This work explores the potential of combining MARL
with Large Language Model (LLM)-mediated interventions to guide agents toward
more desirable behaviours. Specifically, we investigate how LLMs can be used to
interpret and facilitate interventions that shape the learning trajectories of
multiple agents. We experimented with two types of interventions, referred to
as controllers: a Natural Language (NL) Controller and a Rule-Based (RB)
Controller. The NL Controller, which uses an LLM to simulate human-like
interventions, showed a stronger impact than the RB Controller. Our findings
indicate that agents particularly benefit from early interventions, leading to
more efficient training and higher performance. Both intervention types
outperform the baseline without interventions, highlighting the potential of
LLM-mediated guidance to accelerate training and enhance MARL performance in
challenging environments.