Search Results for author:
Found 2 papers, 2 papers with code
Doubly Inhomogeneous Reinforcement Learning
Moreover, by borrowing information over time and population, it allows us to detect weaker signals and has better convergence properties when compared to applying the clustering algorithm per time or the change point detection algorithm per subject.
Testing Stationarity and Change Point Detection in Reinforcement Learning
Based on the proposed test, we further develop a sequential change point detection method that can be naturally coupled with existing state-of-the-art RL methods for policy optimization in nonstationary environments.
Cannot find the paper you are looking for? You can
Submit a new open access paper.
