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20182022
most citedConservative Q-Improvement: Reinforcement Learning for an Interpretable Decision-Tree Policy

14 citations · 14 across the 3 of their papers we have counts for

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cs.RO2022

MSVIPER: Improved Policy Distillation for Reinforcement-Learning-Based Robot Navigation

Aaron M. Roth, Jing Liang, Ram Sriram +2

We present Multiple Scenario Verifiable Reinforcement Learning via Policy Extraction (MSVIPER), a new method for policy distillation to decision trees for improved robot navigation…

cs.RO2021

XAI-N: Sensor-based Robot Navigation using Expert Policies and Decision Trees

Aaron M. Roth, Jing Liang, Dinesh Manocha

We present a novel sensor-based learning navigation algorithm to compute a collision-free trajectory for a robot in dense and dynamic environments with moving obstacles or targets.…