Dynamically Optimal Treatment Allocation using Reinforcement Learning
Dynamic decisions are pivotal to economic policy making. We show how existing evidence from randomized control trials can be utilized to guide personalized decisions in challenging dynamic environments with constraints such as limited budget or queues. Recent developments in reinforcement learning make it possible to solve many realistically complex settings for the first time. We allow for restricted policy functions and prove that their regret decays at rate $n^{-1/2}$, the same as in the static case. We illustrate our methods with an application to job training. The approach scales to a wide range of important problems faced by policy makers.
PDF Abstract
Datasets
Add Datasets
introduced or used in this paper
Results from the Paper
Submit
results from this paper
to get state-of-the-art GitHub badges and help the
community compare results to other papers.
Methods
No methods listed for this paper. Add
relevant methods here
