arXiv Statistics ML
Minimax Regret for Sobolev Kernel-Based Reinforcement Learning
Read the original on arXiv Statistics ML →The Flow has not summarised this story yet — read it at arXiv Statistics ML.
Related stories
arXiv Machine Learning
Jul 23
Asymptotically Optimal Regret for Reinforcement Learning without Horizon Dependence
arXiv:2607. 19854v1 Announce Type: new Abstract: We study horizon-free regret minimization for finite-horizon time-homogeneous tabular Markov decision processes with $S$ states, $A$ actions, horizon $H$, and per-trajectory total reward bounded by $1$.
arXiv Machine Learning
1d ago
Near-Minimax Sample Complexity for Multi-Objective Reinforcement Learning under Generative Models
arXiv:2611.05562v1 Announce Type: cross Abstract: In multi-objective reinforcement learning, preferences may be specified only after data collection, requiring one dataset to support a continuum of r...
arXiv Machine Learning
Sep 24
Limiting-Kernel Q($\lambda$): Bridging Short and Long Horizons
Limiting‑Kernel Q(λ) (LKQL) is an off‑policy value estimator that blends n‑step truncation with a long‑horizon approximation based on the limiting kernel. It maintains the computational efficiency of n‑step methods while improving policy evaluation accuracy, especially for long‑horizon tasks. The authors prove faster convergence of LKQL’s operator under aperiodicity and near‑on‑policy conditions, and demonstrate empirical gains on MuJoCo continuous‑control benchmarks.
arXiv Machine Learning
Sep 17
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.
Hugging Face Trending Papers
Sep 23
Limiting-Kernel Q($λ$): Bridging Short and Long Horizons
In value-based reinforcement learning, improving the accuracy of policy evaluation has been shown to improve downstream policy optimization performance. The widely adopted family of approximations rel...
arXiv Machine Learning
Sep 24
Value Mirror Descent for Reinforcement Learning
arXiv:2604. 06039v2 Announce Type: replace-cross Abstract: Value iteration-type methods have been extensively studied for computing a nearly optimal value function in reinforcement learning (RL).