Modelling Agent Policies with Interpretable Imitation Learning

EasyChair Preprint 2959

Abstract

As we deploy autonomous agents in safety-critical domains, it becomes important to develop an understanding of their internal mechanisms and representations. We outline an approach to imitation learning for reverse-engineering black box agent policies in MDP environments, yielding simplified, interpretable models in the form of decision trees. As part of this process, we explicitly model and learn agents’ latent state representations by selecting from a large space of candidate features constructed from the Markov state. We present initial promising results from an implementation in a multi-agent traffic environment.

Keyphrases: Decision Tree, Explainable Artificial Intelligence, Imitation Learning, Representation Learning, interpretability, traffic modelling

@booklet{EasyChair:2959,
  author    = {Tom Bewley and Jonathan Lawry and Arthur Richards},
  title     = {Modelling Agent Policies with Interpretable Imitation Learning},
  howpublished = {EasyChair Preprint 2959},
  year      = {EasyChair, 2020}}