Alba V. Olivares-Nadal

Thompson Sampling for Infinite-Horizon Discounted Decision Processes

This paper develops a framework for learning in discounted infinite-horizon Markov decision processes (MDPs) with Borel state and action spaces, whose rewards and transitions depend on an unknown parameter, and introduces a canonical probability space that explicitly incorporates sampled parameters into the history of...