Agentic Reinforcement Learning
How autonomous agents learn to reason, plan, use tools, and coordinate through interaction and feedback, especially in long-horizon and multi-agent settings.
TensorFieldX / About me
Hi, I’m
Feixiang Tao.
Artificial Intelligence · SUSTech · Shenzhen
I’m an undergraduate at Southern University of Science and Technology, interested in how learning agents reason, plan, and make reliable decisions.
I am interested in reinforcement learning and long-horizon planning, and in the theory that explains why modern learning systems work.
Learning to reason, plan, and act. From first principles to experiments. Physics, mathematics, and computer science. A space for ideas and questions.
About
Research Interests
Learning Theory
The principles behind modern learning systems, including optimization, generalization, interpretability, and reliable decision-making.
Education
Southern University of Science and Technology
Undergraduate · Academic focus in Artificial Intelligence