LLM-Mediated Guidance of MARL Systems
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.
67dc6adbdb4bc39b0ba2a4e6
Philipp D. Siedler
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2025-03-20T19:22:48.113Z
62a1e17591f85abff79c2cdf
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Philipp Siedler
user
philippds
67dc6adbdb4bc39b0ba2a4e7
Ian Gemp
LLM-Mediated Guidance of MARL Systems
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.
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.
Multi-Agent Reinforcement Learning (MARL) Large Language Model (LLM) Natural Language (NL) Controller Rule-Based (RB) Controller