arXiv AI
Second-Order Smooth Planning with Optimal-Transport Bellman Smoothing
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Minimax PAC Bounds for Learning in Exogenous Contextual MDPs
arXiv:2606. 25170v1 Announce Type: cross Abstract: We study PAC learning in tabular discounted Markov decision processes with exogenous i.
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Unregularized Convergence of Single-Loop, Entropy-Regularized Natural Actor-Critic
While entropy regularization is widely used to stabilize and accelerate Natural Policy Gradient methods, its ability to yield faster convergence rates for the unregularized objective remains underexplored. Existing analyses often rely on double-loop architectures and invoke a linear entropy penalty.
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Unregularized Convergence of Single-Loop, Entropy-Regularized Natural Actor-Critic
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A Convergence Framework for Deep $V$-Learning: Error Propagation and Sharp Action-Gap Bounds
The paper presents a convergence framework for deep $V$‑learning over a finite horizon $H$, deriving explicit bounds on policy loss by decomposing the Bellman update error into six residuals. It shows how $L^s$ concentrability controls expected $L^1$ loss, quantifies the impact of shared sampling across horizon levels, and provides optimal and near‑optimal sample allocations for statistical error rates. The work also establishes sharp action‑gap bounds under a margin condition, transfers optimal‑gap results to frozen‑iterate gaps, and offers consistency guarantees for generative‑reset approximate‑ERM procedures with exact action scores.
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Optimal Sample Complexity of Stable Discounted Markov Decision Processes
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SGHA: A Single-Loop Fully First-Order Algorithm for Nonconvex-Strongly-Convex Bilevel Optimization
In this work, we study the oracle complexity of finding an $ε$-stationary point for nonconvex-strongly-convex (NC-SC) bilevel optimization using only first-order oracles. Existing methods achieving th...