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English(EN) Embedding Information in Policy Randomness: Distribution-Preserving Implicit Communication in MDPs

新方法将信息嵌入AI策略随机性中

研究人员引入了一种名为后验匹配随机性调制(PRAM)的新方法,用于在马尔可夫决策过程(MDP)的随机策略的随机动作中嵌入信息。该技术允许通过调整动作实现来隐式地传达信息,而无需改变整体动作分布。PRAM被视为一个信道编码问题,并被证明是渐进可靠的,能够随着状态轨迹的延长以最小的错误进行消息解码。该论文展示了PRAM在多比特策略水印和轨迹溯源等应用中的效用。 AI

影响 引入了一种用于AI策略中隐式通信的新技术,可能增强AI模型的安全性和可追溯性。

排序理由 关于在AI策略中嵌入新方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.AI 阅读 →

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新方法将信息嵌入AI策略随机性中

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关于在AI策略中嵌入新方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.AI TIER_1 English(EN) · Gongpu Chen, Deniz G\"und\"uz ·

    在策略随机性中嵌入信息:MDP中的分布保持隐式通信

    arXiv:2610.15976v1 Announce Type: cross Abstract: This paper investigates a new role of randomness in stochastic policies--as a medium for embedding and communicating information. Specifically, we consider an agent interacting with an environment according to a prescribed stochas…