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Research map / Reinforcement learning / Policy gradient and actor-critic

Accountable Off-Policy Evaluation With Kernel Bellman Statistics

ICML 2020 · Reinforcement learning › Policy gradient and actor-critic · approach: Confidence intervals and OPE benchmarks · 6 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Increase the number of transitionsKernel Bellman confidence bounds · Inverted-Pendulumconfidence interval widthHelped
Increase the failure probabilityKernel Bellman confidence bounds · Inverted-Pendulumconfidence interval widthHelped
Vary the behavior-policy temperatureKernel Bellman confidence bounds · Inverted-Pendulumconfidence boundsNo clear effect
Use fewer features to constrain the Q-function to a lower-dimensional function spaceKernel Bellman confidence bounds · Inverted-Pendulumconfidence interval widthHelped
Use fewer featuresKernel Bellman confidence bounds · Inverted-Pendulumrisk of model misspecificationHurt
Vary the number of featuresKernel Bellman confidence bounds · Puck-Mountainconfidence boundsMixed
Apply post-hoc diagnosis and correction to an existing Q-estimator that falls outside the confidence intervalExisting Q-estimators · Inverted-Pendulumcorrected estimate validityHelped
Apply post-hoc diagnosis to an existing Q-estimator already inside the confidence intervalExisting Q-estimators · Inverted-Pendulumdebias-term normNo clear effect
Increase the number of transitionsPost-hoc correction · Puck-Mountaindebias-term normMixed

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