2 citations · 4 across the 2 of their papers we have counts for
2 papers
cs.RO2023★ 2 cited
Interpretable Reinforcement Learning for Robotics and Continuous Control
Rohan Paleja, Letian Chen, Yaru Niu +10
Interpretability in machine learning is critical for the safe deployment of learned policies across legally-regulated and safety-critical domains. While gradient-based approaches i…
cs.LG2022★ 2 cited
Learning Interpretable, High-Performing Policies for Autonomous Driving
Rohan Paleja, Yaru Niu, Andrew Silva +3
Gradient-based approaches in reinforcement learning (RL) have achieved tremendous success in learning policies for autonomous vehicles. While the performance of these approaches wa…