Research map / Reinforcement learning / Policy gradient and actor-critic
Off-Policy Interval Estimation with Lipschitz Value Iteration
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| increase the number of off-policy samples (ablation) | Lipschitz value iteration · Synthetic environment with known value function | interval tightness | Helped |
| use a sufficiently large subsample size (ablation) | Lipschitz value iteration · Synthetic environment with known value function | interval tightness | No clear effect |
| increase the number of off-policy samples (ablation) | Lipschitz value iteration · Pendulum | interval tightness | Helped |
| use subsampling rather than full-sample updates (ablation) | Lipschitz value iteration · Pendulum | interval tightness | No clear effect |
| use a sufficiently large subsample size (ablation) | Lipschitz value iteration · HIV simulator | interval tightness | No clear effect |
Closest papers by meaning
- Non-asymptotic Confidence Intervals of Off-policy Evaluation: Primal and Dual Bounds (ICLR 2021 · similarity 0.90)
- Minimax Value Interval for Off-Policy Evaluation and Policy Optimization (NeurIPS 2020 · similarity 0.89)
- Off-Policy Risk Assessment in Contextual Bandits (NeurIPS 2021 · similarity 0.88)
- Deeply-Debiased Off-Policy Interval Estimation (ICML 2021 · similarity 0.88)
- Off-policy Evaluation Beyond Overlap: Sharp Partial Identification Under Smoothness (ICML 2024 · similarity 0.87)
- Efficient and Sharp Off-Policy Evaluation in Robust Markov Decision Processes (NeurIPS 2024 · similarity 0.86)
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