Beyond One-Size-Fits-All: Diagnosis-Driven Online Reinforcement Learning with Offline Priors
Let deployment evidence guide how online learners use and adapt their offline priors.
Research, in full
Publications
Working toward reinforcement learning agents that scale, adapt, and keep learning.
6 publications
2026
What Makes Value Learning Efficient in Residual Reinforcement Learning?
Helping residual RL critics learn from a pretrained policy and recognize small action corrections.
2025
Network Sparsity Unlocks the Scaling Potential of Deep Reinforcement Learning
Randomly pruning a network before training improves how deep RL scales.
A Comprehensive Survey of Data Augmentation in Visual Reinforcement Learning
A framework for understanding how augmented data supports visual reinforcement learning.
2024
Revisiting Plasticity in Visual Reinforcement Learning: Data, Modules and Training Stages
Understanding when and where visual RL loses its ability to keep learning.
2023
Learning Better with Less: Effective Augmentation for Sample-Efficient Visual Reinforcement Learning
Balancing visual diversity and training stability with Rand PR and CycAug.