arXiv:2602.19373v3 Announce Type: replace 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
The paper provides a theoretical justification for improving stability in deep reinforcement learning, a critical bottleneck in the field's advancement, emerging as computational power allows for more complex AI systems to be trained.
Improved stability in deep reinforcement learning could unlock more reliable and adaptive AI systems, accelerating progress across various applications that rely on autonomous decision-making and learning.
Previously, unstable training was a major hurdle for DRL; this research offers a provable method (isotropic Gaussian embeddings) to achieve more adaptive and stable learning for linear readouts.
- · AI research labs
- · Robotics companies
- · Autonomous systems developers
- · AI agents developers
- · Companies relying on less stable DRL methods
- · Research tracks focused on alternative stabilization techniques
More robust and generalizable AI models become achievable, reducing development cycles and improving deployment reliability.
The increased stability could lead to a proliferation of sophisticated AI agents capable of operating in dynamic, real-world environments with less human oversight.
These more adaptive AI systems could potentially accelerate scientific discovery and automate complex processes currently requiring extensive human intervention.
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Read at arXiv cs.LG