DORA The Explorer: Directed Outreaching Reinforcement Action-Selection.

Exploration is a fundamental aspect of Reinforcement Learning. Two key challenges are how to focus exploration on more valuable states, and how to direct exploration toward gaining new world knowledge. Visit-counters have been proven useful both in practice and in theory for directed exploration. However, a major limitation of counters is their locality, considering only the immediate one step exploration value. While there are a few model-based solutions to this difficulty, a model-free approach is still missing. We propose $E$-values, a generalization of counters that can be used to evaluate the propagating exploratory value over state-action trajectories. We compare our approach to commonly used RL techniques, and show that using $E$-value improves learning and performance over traditional counters. We also show how our method can be implemented with function approximation to learn continuous MDPs.