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English(EN) FlexMARL: Graph-Driven Rollout-Training Co-Design for LLM-Based Multi-Agent Reinforcement Learning

FlexMARL框架增强了基于LLM的多智能体强化学习基础设施

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员推出FlexMARL，一个旨在解决大规模多智能体强化学习（MARL）基础设施挑战的新框架，特别针对基于LLM的系统。FlexMARL采用图驱动的方法，将多智能体执行图（MEG）概念化为一个中心数据中心。该框架旨在通过采用依赖并行采样方法、分层负载均衡和智能体级别的异步流水线来克服Rollout偏差和同步障碍等问题。在生产集群上的实证测试表明，与现有框架相比，FlexMARL实现了显著的加速和更高的硬件利用率。 AI

影响 该框架可以显著提高复杂多智能体AI系统训练的效率和可扩展性，可能加速机器人和游戏AI等领域的研究和开发。

排序理由 该集群描述了一篇详细介绍多智能体强化学习新框架的研究论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

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FlexMARL框架增强了基于LLM的多智能体强化学习基础设施

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该集群描述了一篇详细介绍多智能体强化学习新框架的研究论文。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.LG TIER_1 English(EN) · Zhida Jiang, Zhaolong Xing, Jiawei Lu, Yipei Niu, Qingyuan Sang, Liangxu Zhang, Wenquan Dai, Junhua Shu, Jiaxing Wang, Qiangyu Pei, Qiong Chen, Xinyu Liu, Fangming Liu, Ai Han, Zhen Chen, Ke Zhang ·

    FlexMARL：基于图驱动的LLM多智能体强化学习的Rollout-Training协同设计

    arXiv:2602.09578v2 Announce Type: replace Abstract: Despite algorithm-level innovations for multi-agent reinforcement learning (MARL), the underlying infrastructure for large-scale MARL remains underexplored. Existing training frameworks primarily optimize for single-agent scenar…

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