On Distributional Reinforcement Learning in Chaotic Dynamical Systems

Chaotic dynamical systems pose a fundamental challenge for Reinforcement Learning (RL): exponential sensitivity to initial conditions induces high-variance bootstrap targets and poorly conditioned gradient updates. Chaotic dynamics arise across scientific and engineering domains, from fluid flows and climate systems to multi-agent systems, where reliable learning is highly desirable. Standard RL methods optimise expected returns through scalar value functions, implicitly averaging over diverging

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ethics.ai (28 May 2026), “On Distributional Reinforcement Learning in Chaotic Dynamical Systems,” evidence record 3493, https://ethics.ai/record/3493 (originally published by arXiv).

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