Chace Ritchie

Papers

Total Citations

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About

Chace Ritchie is a rising researcher at the intersection of reinforcement learning (RL), autonomous driving, and interpretable AI. His work tackles a critical tension in modern robotics: how to achieve high-performance control policies while maintaining the transparency required for safety-critical deployment. Ritchie’s most cited paper, “Learning Interpretable, High-Performing Policies for Autonomous Driving” (2022), has accumulated 13 citations and directly addresses this challenge by proposing gradient-based RL methods that produce policies that are both effective and human-understandable. He extends this line of inquiry in “Interpretable Reinforcement Learning for Robotics and Continuous Control” (2023), applying similar principles to continuous control problems in robotics. Ritchie’s contributions are particularly timely as autonomous systems face increasing regulatory scrutiny—his work provides a pathway for deploying learned policies in legally-regulated domains where black-box models are unacceptable. By demonstrating that interpretability need not come at the cost of performance, Ritchie is helping shape a future where autonomous vehicles and robots can be both powerful and trustworthy.