Learning Interpretable, High-Performing Policies for Continuous Control Problems

Gradient-based approaches in reinforcement learning (RL) have achieved tremendous success in learning policies for continuous control problems. While the performance of these approaches warrants real-world adoption in domains, such as in autonomous driving and robotics, these policies lack interpretability, limiting deployability in safety-critical and legally-regulated domains. Such domains require 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 autonomous driving scenarios while achieving a $300$x-$600$x reduction in the number of policy parameters against deep learning baselines.