Research map / Reinforcement learning / Policy gradient and actor-critic
Balanced Policy Evaluation and Learning
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| replace propensity-based weighting with optimized balancing weights | policy evaluation · Policy evaluation example | RMSE (vanilla): 0.568 -> 0.280 | Helped |
| replace IPW with balanced doubly robust evaluation | policy evaluation · Policy evaluation example | RMSE (doubly robust): 0.428 -> 0.251 | Helped |
| use optimized balancing weights rather than SNPOEM | linear policy learning · Glass | regret: 0.615 -> 0.584 | Helped |
| use optimized balancing weights rather than SNPOEM | linear policy learning · Ecoli | regret: 0.331 -> 0.298 | Helped |
| use the doubly robust estimator instead of the vanilla estimator (ablation) | balanced policy learner · Glass | regret: 0.584 -> 0.660 | Hurt |
| use the doubly robust estimator instead of the vanilla estimator (ablation) | balanced policy learner · Ecoli | regret: 0.298 -> 0.371 | Hurt |
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