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LLM-based Multi-Agent Reinforcement Learning: Current and Future Directions

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arxiv 2405.11106 v1 pith:67QAZYTL submitted 2024-05-17 cs.MA cs.AIcs.CLcs.LGcs.RO

Chuanneng Sun , Songjun Huang , Dario Pompili This is my paper · ORCID
classification cs.MAcs.AIcs.CLcs.LGcs.RO
keywords llm-basedresearchmulti-agentagentscommunicationdirectionsframeworksfuture
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In recent years, Large Language Models (LLMs) have shown great abilities in various tasks, including question answering, arithmetic problem solving, and poem writing, among others. Although research on LLM-as-an-agent has shown that LLM can be applied to Reinforcement Learning (RL) and achieve decent results, the extension of LLM-based RL to Multi-Agent System (MAS) is not trivial, as many aspects, such as coordination and communication between agents, are not considered in the RL frameworks of a single agent. To inspire more research on LLM-based MARL, in this letter, we survey the existing LLM-based single-agent and multi-agent RL frameworks and provide potential research directions for future research. In particular, we focus on the cooperative tasks of multiple agents with a common goal and communication among them. We also consider human-in/on-the-loop scenarios enabled by the language component in the framework.

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Forward citations

Cited by 12 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Agentic Memory: Learning Unified Long-Term and Short-Term Memory Management for Large Language Model Agents

    cs.CL 2026-01 unverdicted novelty 6.0 of 10

    AgeMem unifies long-term and short-term memory management in LLM agents by exposing memory operations as learnable tool actions trained via three-stage progressive reinforcement learning, outperforming baselines on lo...

  2. MasHost Builds It All: Autonomous Multi-Agent System Directed by Reinforcement Learning

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    MasHost uses reinforcement learning to autonomously construct query-adaptive multi-agent graphs, and its authors report the best average accuracy across six LLM benchmarks.

  3. From Virtual Agents to Robot Teams: A Multi-Robot Framework Evaluation in High-Stakes Healthcare Context

    cs.RO 2025-06 conditional novelty 6.0 of 10

    Adding a structured knowledge base raised a simulated CrewAI healthcare robot team's process score from 45.29% to 72.94%, but five failure modes, including false completion and poor recovery, persisted.

  4. A MARL Centered Reference Architecture for Large Language Model Augmentation in Smart Manufacturing

    cs.AI 2026-08 conditional novelty 5.0 of 10

    The paper proposes a MARL-centered three-layer reference architecture for LLM augmentation in smart manufacturing, with a conditional allocation: MARL for frequent coordination, LLMs for semantic, reward, and planning roles.

  5. Reason Before You Retrieve: Agentic Planning for Multi-modal RAG

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    MM-R2 claims SOTA multimodal RAG accuracy on InfoSeek and Encyclopedic VQA via intent grounding plus a 10-topic KnowledgeMap, but its teacher trajectories leak the gold Wikipedia page and omit the image.

  6. STMA: A Spatio-Temporal Memory Agent for Long-Horizon Embodied Task Planning

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    A spatio-temporal memory agent combining a textual history summarizer, a spatial knowledge graph, and a planner-critic loop outperforms ReAct, Reflexion, and AdaPlanner on TextWorld cooking tasks.

  7. LLM-Driven Policy Diffusion: Enhancing Generalization in Offline Reinforcement Learning

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    LLMDPD conditions an offline policy-diffusion model on LLM-embedded text task descriptions and a transformer-encoded trajectory prompt, reporting improved success on unseen Meta-World and D4RL tasks, though the evalua...

  8. RALLY: Role-Adaptive LLM-Driven Yoked Navigation for Agentic UAV Swarms

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    RALLY couples a two-stage LLM consensus module with a QMIX-style role-assignment network and reports higher reward and better generalization than three baselines in drone-swarm coverage simulations.

  9. LLM-MedQA: Enhancing Medical Question Answering through Case Studies in Large Language Models

    cs.CL 2024-12 reject novelty 4.0 of 10

    A multi-agent Llama-3.1-70B system generates supportive clinical cases before voting and reports 77.2% accuracy on a 300-question MedQA sample, about 7% relative above zero-shot baselines.

  10. Large language models for artificial general intelligence (AGI): A survey of foundational principles and approaches

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    This survey argues that embodiment, symbol grounding, causality, and memory are the foundational principles needed to make large language models achieve artificial general intelligence.

  11. A Survey of the State-of-the-Art in Conversational Question Answering Systems

    cs.CL 2025-09 conditional novelty 2.0 of 10

    A review that categorizes ConvQA components, techniques, models, and datasets, with no new experimental result.

  12. A Decade of Deep Learning: A Survey on The Magnificent Seven

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    This is a review of seven influential deep learning models that does not present new research results and suffers from methodological and integrity issues.

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