Shaofei Chen — accepted AI-conference papers

2025

Meta-Reinforcement Learning With Evolving Gradient Regularization

Jiaxing Chen, Ao Ma, Shaofei Chen, Weilin Yuan, Zhenzhen Hu, Peng Li

RA-L 2025

Deep reinforcement learning (DRL) typically requires reinitializing training for new tasks, limiting its generalization due to isolated knowledge transfer. Meta-reinforcement learning (Meta-RL) addresses this by enabling rapid adaptation through prior task experiences, yet existing gradient-based me

Cited by 1 Source ↗Scholar ↗

2024

Offline Meta-Reinforcement Learning with Evolving Gradient Agreement

Jiaxing Chen, Weilin Yuan, Shaofei Chen, Furong Liu, Ao Ma, Zhenzhen Hu +1

IROS 2024poster

Meta-Reinforcement Learning (Meta-RL) is a machine learning paradigm aimed at learning reinforcement learning policies that can quickly adapt to unseen tasks with few-shot data. Nevertheless, applying Meta-RL to real-world applications faces challenges due to the cost of data acquisition. To address…

Cited by 0 Source ↗Scholar ↗