Decision-Focused On-Policy Learning for Contextual Linear Optimization with Partial Feedback
This research develops a learning method for sequential decision-making under partial feedback in contextual linear optimization. The method learns a policy that predicts costs and then optimizes decisions, using a hybrid gradient estimator combining an unbiased score function term with a decision-focused plug-in term to exploit the problem's structure. This approach achieves competitive regret bounds against standard methods.