This is a Python implementation of the methodology described in the paper:
Low-Rank Bandit Methods for High-Dimensional Dynamic Pricing
Jonas Mueller, Vasilis Syrgkanis, Matt Taddy. NeurIPS (2019)
Each of the main functions in LowRankPricing.py proposes prices for all the products in each round, based on the demands observed from the previous round.
example.py shows how one can use these functions for dynamic pricing (in an environment with simulated demands where the true optimal prices and resulting regret can be determined).