Reviews: REFUEL: Exploring Sparse Features in Deep Reinforcement Learning for Fast Disease Diagnosis

The authors describe an RL architecture comprised of reward shaping plus representation learning that is used to solve an active classification problem, framed as "diagnosis." In this setting, an agent can measure the value of "symptoms" at some cost, and eventually makes a prediction of what disease is present. The architecture is intended to take advantage of the property that symptoms are sparse but correlated. Reward shaping is used to help the agent learn to quickly find symptoms that are present, while the correlations are used to avoid having the agent measure symptoms that are already "known" based on already-measured ones with high certainty. Experimental results demonstrate a substantial improvement over prior work.