arXiv cs.AI· Gongpu Chen, Deniz G\"und\"uz·· 3 天前AI 评分22
策略随机性中的信息嵌入：MDP 中保持分布不变的隐式通信
Embedding Information in Policy Randomness: Distribution-Preserving Implicit Communication in MDPs
AI 导读
研究提出将随机策略的随机性作为信息嵌入与通信媒介，在保持预设动作分布不变的前提下，通过调制随机动作实现隐式通信。所提 PRAM（后验匹配随机性调制）方案借助与控制无关的公共随机性，对每个状态和消息都保持预设动作分布，并证明随状态轨迹足够长，消息可以任意小错误概率解码。实验展示了该框架在多比特策略水印和轨迹溯源等应用中的有效性。
正文 · 原文
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 stochastic policy. By carefully modulating the random action realizations of the policy, information can be embedded into the agent's behavior without altering the prescribed action distribution. We cast this problem as a channel coding problem, in which the controlled system implicitly serves as a communication channel. We then propose a coding scheme, termed posterior-matching randomness modulation (PRAM), that selects actions according to the message to be transmitted. With the aid of common randomness independent of the control system, PRAM preserves the prescribed action distribution for every state and every message. We further prove that PRAM is asymptotically reliable: the message can be decoded with arbitrarily small error probability as the observed state trajectory becomes sufficiently long. Experiments demonstrate the effectiveness of the proposed framework in applications including multi-bit policy watermarking and trajectory provenance.
| Comments: | 18 pages, 6 figures |
| Subjects: | Information Theory (cs.IT); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2610.15976 [cs.IT] |
| (or arXiv:2610.15976v1 [cs.IT] for this version) | |
| https://doi.org/10.48550/arXiv.2610.15976 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Gongpu Chen [view email]
[v1]
Sun, 11 Oct 2026 23:20:39 UTC (340 KB)
来源：arXiv cs.AI · arxiv.org