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Offline Reinforcement Learning for Optimizing Production Bidding Policies
We use a hybrid agent architecture that combines arbitrary base policies with deep neural networks, where only the optimized base policy parameters are eventually deployed, and the neural network part is discarded after training.
Hedonic Prices and Quality Adjusted Price Indices Powered by AI
To accomplish this, we generate abstract product attributes, or ``features,'' from text descriptions and images using deep neural networks, and then use these attributes to estimate the hedonic price function.
Periodic-GP: Learning Periodic World with Gaussian Process Bandits
We consider the sequential decision optimization on the periodic environment, that occurs in a wide variety of real-world applications when the data involves seasonality, such as the daily demand of drivers in ride-sharing and dynamic traffic patterns in transportation.
