Stable Deep Reinforcement Learning via Isotropic Gaussian Representations

TL;DR

This paper introduces isotropic Gaussian embeddings and a regularization method to enhance stability and adaptability in deep reinforcement learning under non-stationary conditions.

Contribution

It proposes Sketched Isotropic Gaussian Regularization to shape representations, improving stability and performance in non-stationary reinforcement learning environments.

Findings

Improved stability and performance in various domains.

Reduced representation collapse and neuron dormancy.

Enhanced adaptability of agents.

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.