Stable Deep Reinforcement Learning via Isotropic Gaussian Representations 事件

Stable Deep Reinforcement Learning via Isotropic Gaussian Representations arXiv:2602.19373v3 Announce Type: replace-cross Abstract: Deep reinforcement learning systems often suffer from unstable training dynamics due to non-stationarity, where learning objectives and data distributions evolve over time. We show that under non-stationary targets, isotropic Gaussian embeddings are provably advantageous. In particular, they induce stable tracking of time-varying targets for linear readouts, achiev