Unverified50% confidenceFactExact time
The recurrent state space model (RSSM) consists of a deterministic recurrent path (GRU) and a stochastic path (latent variables): the deterministic state h_t is updated by the previous moment's h_{t-1} and action a_{t-1} through an RNN, and the stochastic state z_t is sampled from a prior distribution parameterized by h_t; this hybrid design balances long-range memory and multimodal uncertainty expression.
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50%
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Long-term
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10/12/2026
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UnverifiedDreamerV3采用的RSSM(循环状态空间模型)由Danijar Hafner等人提出,将隐状态分解为确定性循环状态和随机隐变量74% similarUnverifiedDreamer系列(Hafner等人,2019-2023)核心是循环状态空间模型(RSSM),通过重构观测学习表征,属于生成式方法67% similarUnverifiedSSM 通过随时间演化的隐藏状态 h_t = A·h_{t-1} + B·x_t 压缩历史信息,输出由 h_t 线性映射得到,单步解码计算量与序列长度无关,显存占用恒定。66% similarUnverifiedState Space Models (SSMs) pull the direction back toward fixed-size recurrent memory, but use a different state structure and update rules.65% similarUnverifiedState space models (SSMs) have O(1) memory footprint and computation during inference, whereas the KV cache of Transformers grows linearly with sequence length.64% similar
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