Research map / Reinforcement learning / MDPs and planning
Scalable Bilinear Pi Learning Using State and Action Features
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 |
|---|---|---|---|
| Represent the value function linearly and the stationary state-action distribution bilinearly using state and action features | Bilinear π learning for average-reward MDPs · Average-reward MDPs with a sampling oracle | sample complexity: O(DU log(DU) / ϵ²) | Helped |
| Use features whose value-function and state-action-distribution approximation errors may be nonzero | Bilinear π learning · Unrealizable average-reward MDPs | average-reward suboptimality | Mixed |
Closest papers by meaning
- Bilinear Classes: A Structural Framework for Provable Generalization in RL (ICML 2021 · similarity 0.90)
- Overcoming the Curse of Dimensionality in Reinforcement Learning Through Approximate Factorization (ICML 2025 · similarity 0.86)
- Confident Natural Policy Gradient for Local Planning in $q_\pi$-realizable Constrained MDPs (NeurIPS 2024 · similarity 0.86)
- On the Sample Complexity Bounds of Bilevel Reinforcement Learning (NeurIPS 2025 · similarity 0.86)
- Oracle-Efficient Reinforcement Learning for Max Value Ensembles (NeurIPS 2024 · similarity 0.86)
- A PAC Learning Algorithm for LTL and Omega-Regular Objectives in MDPs (AAAI 2024 · similarity 0.86)
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.