Leveraging Maximum Mean Discrepancy Barycenters to Propagate Uncertainty in Reinforcement Learning Value Functions

The core message of this work is to introduce Maximum Mean Discrepancy Q-Learning (MMD-QL) as a novel algorithm that utilizes MMD barycenters to effectively propagate the uncertainty of value functions during Temporal Difference updates in reinforcement learning, leading to improved exploration and performance.