Agentic Robot Learning
How agents acquire experience and turn it into reliable, reusable robot skills.
I’m Guozheng Ma, a PhD student at Nanyang Technological University, advised by Dacheng Tao. Previously, I completed my master’s studies at Tsinghua University and my bachelor’s degree at Hunan University.
My research focuses on deep reinforcement learning: how agents can learn efficiently, scale, and keep adapting in open-ended environments. I study optimization, network sparsity, plasticity, and the interplay between offline knowledge and online experience. More broadly, I’m interested in RL as a way to understand and develop intelligence.
Greatness cannot be planned, so I play with joyful explorations!
How agents acquire experience and turn it into reliable, reusable robot skills.
Static sparsity offers a surprisingly simple way to improve scaling in deep RL.
Data, the critic and the timing of updates all shape an agent's learning capacity.