Interpretable reinforcement learning with decision-tree pruning
The hosts discuss a paper by Ringer and Tokic on making reinforcement learning policies interpretable by converting neural networks into decision trees and pruning them. They highlight auditable pruning, DACP's reward-aware method, and results showing pruned trees sometimes outperform teachers. They conclude interpretability is an incremental, transparent process.