Research map / Reinforcement learning / Policy gradient and actor-critic
Minimax Weight and Q-Function Learning for Off-Policy Evaluation
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use MQL instead of the compared OPE methods | CartPole · CartPole | log MSE of relative errors | Helped |
| Use MWL instead of MSWL in the tabular setting | Taxi · Taxi | OPE performance | Helped |
| Use MWL or MQL compared with related algorithms | CartPole · CartPole | log MSE of relative errors | Mixed |
Closest papers by meaning
- Variance-Aware Off-Policy Evaluation with Linear Function Approximation (NeurIPS 2021 · similarity 0.91)
- Minimax Value Interval for Off-Policy Evaluation and Policy Optimization (NeurIPS 2020 · similarity 0.91)
- Enhancing Value Function Estimation through First-Order State-Action Dynamics in Offline Reinforcement Learning (ICML 2024 · similarity 0.89)
- Minimax Optimal and Computationally Efficient Algorithms for Distributionally Robust Offline Reinforcement Learning (NeurIPS 2024 · similarity 0.89)
- Q-MMR: Off-Policy Evaluation via Recursive Reweighting and Moment Matching (NeurIPS 2026 · similarity 0.89)
- On the Complexity of Offline Reinforcement Learning with Q*-Approximation and Partial Coverage (NeurIPS 2026 · similarity 0.89)
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