Research map / Reinforcement learning / MDPs and planning
Provably Efficient Reinforcement Learning with Kernel and Neural Function Approximations
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
- Reward-Free Kernel-Based Reinforcement Learning (ICML 2024 · similarity 0.93)
- Kernel-Based Function Approximation for Average Reward Reinforcement Learning: An Optimist No-Regret Algorithm (NeurIPS 2024 · similarity 0.92)
- On Reward-Free RL with Kernel and Neural Function Approximations: Single-Agent MDP and Markov Game (ICML 2021 · similarity 0.91)
- Kernelized Reinforcement Learning with Order Optimal Regret Bounds (NeurIPS 2023 · similarity 0.91)
- Provably Efficient Reinforcement Learning with Multinomial Logit Function Approximation (NeurIPS 2024 · similarity 0.90)
- Oracle-Efficient Reinforcement Learning for Max Value Ensembles (NeurIPS 2024 · similarity 0.89)
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.
