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English(EN) BE-CTDE: Blockchain-Enabled Multi-Agent Reinforcement Learning with Anomaly Detection and Token Economics

区块链通过代币经济学增强多智能体强化学习

作者 PulseAugur 编辑部 · [2 个来源] ·

研究人员推出了一种名为BE-CTDE的新系统，该系统将区块链技术与多智能体强化学习（MARL）相结合，以增强物联网等系统中的协调性和信任度。该方法使用区块链作为训练管理层，并采用行为验证器将智能体行为与策略建议进行比较。一种包含罚没和奖励的代币机制会惩罚偏离行为并激励遵守，将智能体的可审计行为与其对全局策略更新的影响直接联系起来。在多智能体路径寻找方面的实验表明，BE-CTDE在对抗条件下显著提高了成功率，即使在对手占多数的情况下也优于现有方法。 AI

影响 这项研究可能为物联网等分布式环境中的更强大、更值得信赖的AI系统带来突破。

排序理由 该集群包含一篇详细介绍多智能体强化学习新方法的学术论文。

在 arXiv cs.MA (Multiagent) 阅读 →

AI 生成摘要 · Google Gemini · 来自 2 个来源。 我们如何撰写摘要 →

区块链通过代币经济学增强多智能体强化学习

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报道来源 [2]

  1. arXiv cs.LG TIER_1 English(EN) · Chih-Wei Huang, Hsin-Chieh Lee, Yun-Chen Yu, Ibrahim Althamary, Rongxing Lu, Phone Lin ·

    BE-CTDE：支持异常检测和代币经济学的区块链赋能多智能体强化学习

    arXiv:2610.34121v1 Announce Type: cross Abstract: Multi-agent reinforcement learning supports cooperative decision-making in Internet of Things systems, but strategic deviations can disrupt coordination and undermine confidence in the training process. This paper proposes Blockch…

  2. arXiv cs.MA (Multiagent) TIER_1 English(EN) · Phone Lin ·

    BE-CTDE：支持异常检测和代币经济学的区块链赋能多智能体强化学习

    Multi-agent reinforcement learning supports cooperative decision-making in Internet of Things systems, but strategic deviations can disrupt coordination and undermine confidence in the training process. This paper proposes Blockchain-Enabled Centralized Training and Decentralized…

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