Flowing Through States: Neural ODE Regularization for Reinforcement Learning

Neural networks applied to sequential decision-making tasks typically rely on latent representations of environment states. While environment dynamics dictate how semantic states evolve, the corresponding latent transitions are usually left implicit, creating a potential misalignment between the two. We propose to model latent dynamics explicitly by drawing an analogy between Markov decision process (MDP) trajectories and ordinary differential equation (ODE) flows: in both cases, the current sta

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ethics.ai (6 August 2026), “Flowing Through States: Neural ODE Regularization for Reinforcement Learning,” evidence record 17944, https://ethics.ai/record/17944 (originally published by arXiv cs.LG).

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