Speeding up Tabular Reinforcement Learning Using State-Action Similarities

Ariel Rosenfeld, Matthew E. Taylor, Sarit Kraus

Abstract: One of the most prominent approaches for speeding up reinforcement learning is injecting human prior knowledge into the learning agent. This paper proposes a novel method to speed up temporal difference learning by using state-action similarities. These hand-coded similarities are tested in three well-studied domains of varying complexity, demonstrating our approach's benefits.