Thomas Eiter

ORCAID: Oblique Rule-Based Continuous-Action Interpretation for Deep RL Policies

This work introduces ORCAID, a novel method for extracting interpretable rule-based policies from RL agents operating in mixed continuous-discrete environments with continuous action spaces, with an efficient oblique decision tree training algorithm that partitions the state space by hyperplanes and fits local linear models.