3 papers
cs.MA2025
Making Teams and Influencing Agents: Efficiently Coordinating Decision Trees for Interpretable Multi-Agent Reinforcement Learning
Rex Chen, Stephanie Milani, Zhicheng Zhang +2
Poor interpretability hinders the practical applicability of multi-agent reinforcement learning (MARL) policies. Deploying interpretable surrogates of uninterpretable policies enha…
cs.LG2025
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…
cs.CL2024
PATIENT-Ψ: Using Large Language Models to Simulate Patients for Training Mental Health Professionals
Ruiyi Wang, Stephanie Milani, Jamie C. Chiu +9
Mental illness remains one of the most critical public health issues. Despite its importance, many mental health professionals highlight a disconnect between their training and act…