Steering the Feedback Loop: Policy Gradients for Online Performative Optimization
Performative decision-making arises in many real-world problems where decisions influence the populations that generate future data. A major line of existing research focuses on learning performatively stable solutions. We study average-reward performative optimization, where the learner observes an evolving population through sample batches of fixed size and learns from a single continuing trajectory with unknown … Read more