Is Temporal Difference Learning the Gold Standard for Stitching in RL?
NeutralArtificial Intelligence
A recent paper discusses the effectiveness of temporal difference (TD) learning in reinforcement learning (RL), particularly in stitching together experiences from short training data to tackle long-horizon tasks. While TD methods are often seen as the gold standard for this capability, the paper raises questions about their applicability in larger settings where trajectories do not intersect. This exploration is significant as it challenges established beliefs in the field and could lead to new insights on the use of Monte Carlo methods in RL.
— Curated by the World Pulse Now AI Editorial System

