LangMARL: Natural Language Multi-Agent Reinforcement Learning

2026-04-01 · Huaiyuan Yao, Longchao Da, Xiaoou Liu, Charles Fleming, Tianlong Chen, Hua Wei arxiv

Large language model (LLM) agents struggle to autonomously evolve coordination strategies in dynamic environments, largely because coarse global outcomes obscure the causal signals needed for local policy refinement. We identify this bottleneck as a multi-agent credit assignment problem, which has long been studied in classical multi-agent reinforcement learning (MARL) but remains underaddressed in LLM-based systems. Building on this observation, we propose LangMARL, a framework that brings credit assignment and policy gradient evolution from cooperative MARL into the language space. LangMARL introduces agent-level language credit assignment, pioneers gradient evolution in language space for policy improvement, and summarizes task-relevant causal relations from replayed trajectories to provide dense feedback and improve convergence under sparse rewards. Extensive experiments across diverse cooperative multi-agent tasks demonstrate improved sample efficiency, interpretability, and strong generalization.

📄 PDF Abstract BibTeX arXiv:2604.00722

Code (0)

등록된 구현이 없습니다.

Tasks

Multi-agent Reinforcement Learning

Safe Multi-agent Reinforcement Learning with Natural Language Constraints

2024-05-30 · Ziyan Wang, Meng Fang, Tristan Tomilin, Fei Fang 외

The role of natural language constraints in Safe Multi-agent Reinforcement Learning (MARL) is crucial, yet often overlooked. While Safe MARL has vast potential, especially in fields like robotics and autonomous vehicles,…

Autonomous Vehicles Multi-agent Reinforcement Learning reinforcement-learning Reinforcement Learning

Toward Collaborative Reinforcement Learning Agents that Communicate Through Text-Based Natural Language

2021-07-20 · Kevin Eloff, Herman A. Engelbrecht

Communication between agents in collaborative multi-agent settings is in general implicit or a direct data stream. This paper considers text-based natural language as a novel form of communication between multiple agents…

Navigate reinforcement-learning Reinforcement Learning Reinforcement Learning (RL)

Beating Atari with Natural Language Guided Reinforcement Learning

2017-04-18 · Russell Kaplan, Christopher Sauer, Alexander Sosa

We introduce the first deep reinforcement learning agent that learns to beat Atari games with the aid of natural language instructions. The agent uses a multimodal embedding between environment observations and natural l…

Atari Games Deep Reinforcement Learning Montezuma's Revenge OpenAI Gym+3

Influencing Reinforcement Learning through Natural Language Guidance

2021-04-04 · Tasmia Tasrin, Md Sultan Al Nahian, Habarakadage Perera, Brent Harrison

Interactive reinforcement learning agents use human feedback or instruction to help them learn in complex environments. Often, this feedback comes in the form of a discrete signal that is either positive or negative. Whi…

Deep Reinforcement Learning reinforcement-learning Reinforcement Learning Reinforcement Learning (RL)

GLIDE-RL: Grounded Language Instruction through DEmonstration in RL

2024-01-03 · Chaitanya Kharyal, Sai Krishna Gottipati, Tanmay Kumar Sinha, Srijita Das 외

One of the final frontiers in the development of complex human - AI collaborative systems is the ability of AI agents to comprehend the natural language and perform tasks accordingly. However, training efficient Reinforc…

Continual Learning reinforcement-learning Reinforcement Learning Reinforcement Learning (RL)