title: LLM-Mediated Guidance of MARL Systems
summary: 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.
paper_authors._id: 67dc6adbdb4bc39b0ba2a4e6
paper_authors.name: Philipp D. Siedler
paper_authors.status: extracted_confirmed
paper_authors.statusLastChangedAt: 2025-03-20T19:22:48.113Z
paper_authors._id: 62a1e17591f85abff79c2cdf
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/1654776114657-noauth.jpeg
paper_authors.fullname: Philipp Siedler
paper_authors.type: user
paper_authors.user: philippds
paper_authors._id: 67dc6adbdb4bc39b0ba2a4e7
paper_authors.name: Ian Gemp
paper_title: LLM-Mediated Guidance of MARL Systems
paper_summary: 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.
paper_ai_summary: LLM-mediated interventions, particularly those using natural language controllers, enhance Multi-Agent Reinforcement Learning by guiding agents more efficiently, leading to improved training and performance in complex environments.
paper_ai_keywords: Multi-Agent Reinforcement Learning (MARL) Large Language Model (LLM) Natural Language (NL) Controller Rule-Based (RB) Controller