Research map / Reinforcement learning / MDPs and planning
Near-Optimal Reinforcement Learning in Dynamic Treatment Regimes
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 |
|---|---|---|---|
| add causal bounds from confounded observational data (UCc-DTR vs UC-DTR) | UC-DTR · 200 random DTRs | cumulative regret: over 80% | Helped |
| add causal bounds from confounded observational data (UCc-DTR vs UC-DTR) | UC-DTR · Cancer treatment (CALGB 8923) survival model | cumulative regret | Helped |
| use online adaptive UC-DTR instead of randomized assignment | sequential randomized trials · 200 random DTRs | cumulative regret | Helped |
Closest papers by meaning
- Position: Reinforcement Learning in Dynamic Treatment Regimes Needs Critical Reexamination (ICML 2024 · similarity 0.91)
- EpiCare: A Reinforcement Learning Benchmark for Dynamic Treatment Regimes (NeurIPS 2024 · similarity 0.89)
- Iteratively Refined Behavior Regularization for Offline Reinforcement Learning (NeurIPS 2024 · similarity 0.86)
- Evaluating and Learning Optimal Dynamic Treatment Regimes under Truncation by Death (NeurIPS 2025 · similarity 0.86)
- A Snapshot of Influence: A Local Data Attribution Framework for Online Reinforcement Learning (NeurIPS 2025 · similarity 0.85)
- Information-Directed Offline-to-Online Reinforcement Learning (NeurIPS 2026 · similarity 0.85)
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.