Behavior-Induced Mirror-Prox Temporal-Difference Learning for Faster Off-Policy Prediction 事件

Behavior-Induced Mirror-Prox Temporal-Difference Learning for Faster Off-Policy Prediction arXiv:2605.28849v1 Announce Type: new Abstract: Gradient temporal-difference methods provide stable off-policy prediction with linear function approximation, but their practical performance is strongly affected by the geometry induced by the auxiliary-variable metric. Existing Mirror-Prox TD methods typically use the feature covariance metric, whereas hybrid TD methods suggest that behavior-policy transit