  Gradient-based approaches in reinforcement learning (RL) have achieved
tremendous success in learning policies for autonomous vehicles. While the
performance of these approaches warrants real-world adoption, these policies
lack interpretability, limiting deployability in the safety-critical and
legally-regulated domain of autonomous driving (AD). AD requires interpretable
and verifiable control policies that maintain high performance. We propose
Interpretable Continuous Control Trees (ICCTs), a tree-based model that can be
optimized via modern, gradient-based, RL approaches to produce high-performing,
interpretable policies. The key to our approach is a procedure for allowing
direct optimization in a sparse decision-tree-like representation. We validate
ICCTs against baselines across six domains, showing that ICCTs are capable of
learning interpretable policy representations that parity or outperform
baselines by up to 33% in AD scenarios while achieving a 300x-600x reduction in
the number of policy parameters against deep learning baselines. Furthermore,
we demonstrate the interpretability and utility of our ICCTs through a 14-car
physical robot demonstration.
