Learning Interpretable, High-Performing Policies for Autonomous Driving
Rohan Paleja, Yaru Niu, Andrew Silva, Chace Ritchie, Sugju Choi, Matthew Gombolay · 2022
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 highperforming, interpretable policies.The key to our approach is a procedure for allowing direct optimization in a sparse decisiontree-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.i x k -b i > 0).As each leaf node is represented as a probability mass function over output classes in prior work, leaf nodes, l, must be modified to produce