Research map / Reinforcement learning / MDPs and planning
Provably Efficient Q-learning with Function Approximation via Distribution Shift Error Checking Oracle
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.
Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use DMQ with a distribution-shift error-checking oracle to select exploration policies | Q-learning with linear function approximation · Episodic stochastic MDPs | trajectory sample complexity: poly(1/ε) | Helped |
| Use a DSEC oracle that checks predictive error across policy-induced distributions | DMQ · Episodic stochastic MDPs | sample complexity | Helped |
Closest papers by meaning
- A new convergent variant of Q-learning with linear function approximation (NeurIPS 2020 · similarity 0.90)
- A Nearly Optimal and Low-Switching Algorithm for Reinforcement Learning with General Function Approximation (NeurIPS 2024 · similarity 0.89)
- Regularized Q-Learning (NeurIPS 2024 · similarity 0.88)
- Computational Hardness of Reinforcement Learning with Partial $q^{\pi}$-Realizability (NeurIPS 2025 · similarity 0.87)
- Provably Efficient Reinforcement Learning with Multinomial Logit Function Approximation (NeurIPS 2024 · similarity 0.87)
- Linear $Q$-Learning Does Not Diverge in $L^2$: Convergence Rates to a Bounded Set (ICML 2025 · similarity 0.87)
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.