Research map / Reinforcement learning / MDPs and planning
Provably Efficient Offline Reinforcement Learning for Partially Observable Markov Decision Processes
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Pessimistic offline planning over IV-based confidence region for linear POMDP transition model (proposed method) | linear POMDP offline RL · theoretical sample complexity | episodes needed for epsilon-optimal policy | No clear effect |
Closest papers by meaning
- Model-based Reinforcement Learning for Confounded POMDPs (ICML 2024 · similarity 0.93)
- Pessimism in the Face of Confounders: Provably Efficient Offline Reinforcement Learning in Partially Observable Markov Decision Processes (ICLR 2023 · similarity 0.92)
- Near-Optimal Offline Reinforcement Learning via Double Variance Reduction (NeurIPS 2021 · similarity 0.91)
- Off-Policy Evaluation in Partially Observable Environments (AAAI 2020 · similarity 0.91)
- Provable Representation with Efficient Planning for Partially Observable Reinforcement Learning (ICML 2024 · similarity 0.90)
- On the Complexity of Offline Reinforcement Learning with Q*-Approximation and Partial Coverage (NeurIPS 2026 · similarity 0.90)
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