Feng Lu

Explainable Multi-Agent Reinforcement Learning for Temporal Queries

2023 · arXiv (Cornell University)

As multi-agent reinforcement learning (MARL) systems are increasingly deployed throughout society, it is imperative yet challenging for users to understand the emergent behaviors of MARL agents in complex environments. This work presents an approach for …

Counterfactual Explanations for Continuous Action Reinforcement Learning

2025 · arXiv (Cornell University)

Reinforcement Learning (RL) has shown great promise in domains like healthcare and robotics but often struggles with adoption due to its lack of interpretability. Counterfactual explanations, which address "what if" scenarios, provide a promising avenue …