ETD

Emphatic Temporal Difference learning is a value function estimation algorithm designed to stabilize off-policy reinforcement learning. It introduces emphasis weights to redistribute the importance of state updates, geometrically altering the projection direction of TD learning to address divergence that arises when function approximation, bootstrapping, and off-policy training occur simultaneously.