arXiv Machine Learning
Kernel-based potential mean-field games with unbiased random Fourier $U$-statistics
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arXiv Machine Learning
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Convergence, design and training of continuous-time dropout as a random batch method
arXiv:2510.13134v2 Announce Type: replace Abstract: We study continuous-time dropout in controlled differential equations. We introduce a random-batch approximation of additive vector fields. On each...
arXiv Machine Learning
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Towards Scaling Reinforcement Learning to Massive Populations: Learning Mean-Field Representations
The paper proposes a mean‑field reinforcement learning framework that models rewards and transitions as functions of an unknown low‑dimensional aggregate statistic of a large agent population. By learning this low‑dimensional representation in an offline setting, the authors demonstrate a provable method for obtaining near‑optimal policies. Experiments on a one‑step routing game inspired by supply‑chain problems show that, with a fixed neural‑network size and optimization budget, the learned representation improves reward prediction and the quality of Nash equilibria compared to baselines that ignore population structure.
arXiv AI
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Analytic Bridge Diffusions for Controlled Path Generation
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arXiv Machine Learning
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Expressivity and Statistical Trade-offs in Diffusion Policy Learning
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arXiv Machine Learning
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Policy Gradient for Continuous-Time Robust Markov Decision Processes
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arXiv Machine Learning
Jun 30
A Deterministic Sampling Method via Maximum Mean Discrepancy Flow with Adaptive Kernel
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