Research map / Learning theory and algorithms / Kernels and regression theory
A Non-asymptotic Analysis of Non-parametric Temporal-Difference Learning
Read the paperCheck your idea against 108,967 papers
AI-extracted summary. Written by AI from the paper's title only (no summary yet). Numbers and claims can be wrong or missing context, so check the paper before you cite or build on it.
Closest papers by meaning
- Bridging the Gap Between Average and Discounted TD Learning (ICML 2026 · similarity 0.92)
- Towards Parameter-Free Temporal Difference Learning (ICML 2026 · similarity 0.92)
- The surprising efficiency of temporal difference learning for rare event prediction (NeurIPS 2024 · similarity 0.89)
- Almost Sure Convergence of Linear Temporal Difference Learning with Arbitrary Features (NeurIPS 2026 · similarity 0.88)
- Finite Sample Analysis of Linear Temporal Difference Learning with Arbitrary Features (NeurIPS 2025 · similarity 0.88)
- An Improved Finite-time Analysis of Temporal Difference Learning with Deep Neural Networks (ICML 2024 · similarity 0.88)
Writing a paper in this area? scime draft is a free LaTeX editor with AI included, real-time co-authors and a check of your references and venue rules before you submit. It also imports OpenAI Prism projects.
