WebbThe presence of uncertainty in policy evaluation significantly complicates the process of policy ranking and selection in real-world settings. We formally consider offline policy … Webbwe develop an Uncertainty Regularized Policy Learning (URPL) method. URPL adds an uncertainty regularization term in the policy learning objective to enforce to learn a more stable policy under the offline setting. Moreover, we further use the uncertainty regularization term as a surrogate metric indicating the potential performance of a policy.

Policy-Adaptive Estimator Selection for Off-Policy Evaluation

Webb12 dec. 2024 · The presence of uncertainty in policy evaluation significantly complicates the process of policy ranking and selection in real-world settings. Webb1 aug. 2024 · This work presents a guided policy search algorithm that uses trajectory optimization to direct policy learning and avoid poor local optima, and shows how … flower reef

Offline Policy Selection under Uncertainty DeepAI

Webb25 nov. 2024 · Off-policy policy evaluation (OPE) is the problem of estimating the online performance of a policy using only pre-collected historical data generated by another … WebbOffline Policy Selection Offline policy selection: •Compute a ranking O ∈ Perm([1, N]) over given a fixed dataset D according to some utility function u: {π i}N i=1 Offline … WebbIntroduction. In 2024, the COVID-19 pandemic caused a lot of panic buying around the world. Due to the lack of transparency of information in many countries and regions, people were full of panic or even scared due to uncertain information and then proceeded to hoard goods. 1 People in the United States, Italy, and other countries have hoarded a … flower reef png