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This research introduces Sketched Isotropic Gaussian Regularization to stabilize deep reinforcement learning under non-stationary conditions, offering a computationally inexpensive method to mitigate representation collapse and neuron dormancy. By enforcing isotropic Gaussian embeddings, the approach provably improves adaptive tracking of time-varying targets while balancing the utilization of representational dimensions. The technique demonstrates robust empirical gains across multiple domains, addressing a core challenge in training dynamics without incurring significant overhead.
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, achieve maximal entropy under a fixed variance budget, and encourage a balanced use of all representational dimensions--all of which enable agents to be more adaptive and stable. Building on this insight, we propose the use of Sketched Isotropic Gaussian Regularization for shaping representations toward an isotropic Gaussian distribution during training. We demonstrate empirically, over a variety of domains, that this simple and computationally inexpensive method improves performance under non-stationarity while reducing representation collapse, neuron dormancy, and training instability.
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, achieve maximal entropy under a fixed variance budget, and encourage a balanced use of all representational dimensions--all of which enable agents to be more adaptive and stable. Building on this insight, we propose the use of Sketched Isotropic Gaussian Regularization for shaping representations toward an isotropic Gaussian distribution during training. We demonstrate empirically, over a variety of domains, that this simple and computationally inexpensive method improves performance under non-stationarity while reducing representation collapse, neuron dormancy, and training instability.
Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
arXiv:2602.19373 [cs.LG]
(or arXiv:2602.19373v3 [cs.LG] for this version)
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