Robotics: Science and Systems XVIII

Learning Interpretable, High-Performing Policies for Autonomous Driving

Rohan Paleja^*, Yaru Niu^*, Andrew Silva, Chace Ritchie, Sugju Choi, Matthew Gombolay
^* These authors contributed equally

Abstract:

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.

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Bibtex:

  
@INPROCEEDINGS{Paleja-RSS-22, 
    AUTHOR    = {Rohan Paleja AND Yaru Niu AND Andrew Silva AND Chace Ritchie AND Sugju Choi AND Matthew Gombolay}, 
    TITLE     = {{Learning Interpretable, High-Performing Policies for Autonomous Driving}}, 
    BOOKTITLE = {Proceedings of Robotics: Science and Systems}, 
    YEAR      = {2022}, 
    ADDRESS   = {New York City, NY, USA}, 
    MONTH     = {June}, 
    DOI       = {10.15607/RSS.2022.XVIII.068} 
}