3 papers
cs.LG2025
Pref-GUIDE: Continual Policy Learning from Real-Time Human Feedback via Preference-Based Learning
Zhengran Ji, Boyuan Chen
Training reinforcement learning agents with human feedback is crucial when task objectives are difficult to specify through dense reward functions. While prior methods rely on offl…
cs.LG2024
GUIDE: Real-Time Human-Shaped Agents
Lingyu Zhang, Zhengran Ji, Nicholas R Waytowich +1
The recent rapid advancement of machine learning has been driven by increasingly powerful models with the growing availability of training data and computational resources. However…
cs.RO2024
Enabling Multi-Robot Collaboration from Single-Human Guidance
Zhengran Ji, Lingyu Zhang, Paul Sajda +1
Learning collaborative behaviors is essential for multi-agent systems. Traditionally, multi-agent reinforcement learning solves this implicitly through a joint reward and centraliz…