9 citations · 11 across the 4 of their papers we have counts for
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cs.LG2026
Selecting Decision-Relevant Concepts in Reinforcement Learning
Naveen Raman, Stephanie Milani, Fei Fang
Training interpretable concept-based policies requires practitioners to manually select which human-understandable concepts an agent should reason with when making sequential decis…
cs.LG2024
LICORICE: Label-Efficient Concept-Based Interpretable Reinforcement Learning
Zhuorui Ye, Stephanie Milani, Geoffrey J. Gordon +1
Recent advances in reinforcement learning (RL) have predominantly leveraged neural network policies for decision-making, yet these models often lack interpretability, posing challe…