Research map / Reinforcement learning / MDPs and planning
Pessimistic Q-Learning for Offline Reinforcement Learning: Towards Optimal Sample Complexity
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| variance reduction (reference-advantage decomposition) added to pessimistic Q-learning | LCB-Q · finite-horizon offline RL (theory) | sample complexity order: H^6 -> H^4 leading term | Helped |
| the variance-reduced algorithm is near-optimal for epsilon range | LCB-Q-Advantage · finite-horizon offline RL (theory) | epsilon range of minimax optimality: epsilon <= 1/H vs 1/H^2.5 for PEVI-Adv | Helped |
| pessimism (LCB penalty) in Q-learning for offline RL | Q-learning · finite-horizon offline RL (theory) | sample complexity order | Helped |
Closest papers by meaning
- A Unified Principle of Pessimism for Offline Reinforcement Learning under Model Mismatch (NeurIPS 2024 · similarity 0.92)
- Sample Efficient Reinforcement Learning with Partial Dynamics Knowledge (AAAI 2024 · similarity 0.91)
- Information-Directed Pessimism for Offline Reinforcement Learning (ICML 2024 · similarity 0.91)
- Pessimism Meets Risk: Risk-Sensitive Offline Reinforcement Learning (ICML 2024 · similarity 0.91)
- Exclusively Penalized Q-learning for Offline Reinforcement Learning (NeurIPS 2024 · similarity 0.91)
- Pessimistic Nonlinear Least-Squares Value Iteration for Offline Reinforcement Learning (ICLR 2024 · similarity 0.91)
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