Research map / Reinforcement learning / Offline RL
Offline Reinforcement Learning with Differentiable Function Approximation is Provably Efficient
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| variance-aware reweighting of fitted Q regression (VAFQL) instead of PFQL | fitted Q-learning with differentiable function class · theoretical suboptimality bound | horizon dependence of bound | Helped |
| pessimism penalty based on uncertainty Γ_h with differentiable function class (PFQL vs. VFQL) | fitted Q-learning with differentiable function approximation · theoretical suboptimality bound | assumption required / bound form | Helped |
| minimax lower bound shows gap of at most sqrt(d) vs. upper bound for linear case | linear representation f=<θ,φ> · theoretical minimax lower bound | lower bound factor | No clear effect |
Closest papers by meaning
- On the Complexity of Offline Reinforcement Learning with Q*-Approximation and Partial Coverage (NeurIPS 2026 · similarity 0.92)
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- A Unifying View of Linear Function Approximation in Off-Policy RL Through Matrix Splitting and Preconditioning (NeurIPS 2025 · similarity 0.89)
- Neural Network Approximation for Pessimistic Offline Reinforcement Learning (AAAI 2024 · similarity 0.89)
- Exclusively Penalized Q-learning for Offline Reinforcement Learning (NeurIPS 2024 · similarity 0.89)
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