2 papers
cs.LG2026
Generalize and Guide: Decomposing Rewards for Few-Shot Inverse Reinforcement Learning
Ziyi Liu, Grace Zhang
Inverse reinforcement learning (IRL) provides a powerful framework for learning from demonstrations. However, real-world tasks often exhibit substantial natural variations (e.g., p…
cs.LG2025
QMP: Q-switch Mixture of Policies for Multi-Task Behavior Sharing
Grace Zhang, Ayush Jain, Injune Hwang +2
Multi-task reinforcement learning (MTRL) aims to learn several tasks simultaneously for better sample efficiency than learning them separately. Traditional methods achieve this by…