AINeutralarXiv – CS AI · 14h ago6/10
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On Distributional Reinforcement Learning in Chaotic Dynamical Systems
Researchers propose that distributional reinforcement learning offers superior performance in chaotic dynamical systems by measuring return distributions under the 1-Wasserstein metric rather than optimizing scalar expected values. This approach reduces variance and improves gradient conditioning in systems with exponential sensitivity to initial conditions, providing theoretical foundations for applying RL to climate, fluid dynamics, and multi-agent scenarios.