Howard Zhang
Papers
1
Total Citations
4
H-Index
1
About
Howard Zhang is a rising researcher in reinforcement learning and robotics, with a focus on enabling machines to plan and act over extended time horizons. His most-cited work, "Learning Accurate Long-term Dynamics for Model-based Reinforcement Learning" (2021, 4 citations), tackles a fundamental challenge in model-based control: the compounding errors that arise when recursively predicting system states. Zhang proposes a novel approach that directly optimizes for long-horizon prediction accuracy, moving beyond traditional one-step-ahead models to improve the reliability of learned dynamics for robotic systems. This contribution is particularly significant for applications requiring precise, sustained interaction with the physical world, such as manipulation and locomotion. Though early in his career, Zhang’s work has already attracted attention for its practical impact on model-based reinforcement learning pipelines. His research sits at the intersection of control theory, deep learning, and robotics, and he is recognized for advancing methods that bridge the gap between simulation and real-world deployment. As the field increasingly demands robust long-term planning, Zhang’s contributions are poised to influence both academic research and applied autonomous systems.
Research Focus
Key Achievements
Top Papers
- 1