Tianhong Dai
Papers
1
Total Citations
7
H-Index
1
About
Tianhong Dai is a researcher whose work lies at the intersection of deep reinforcement learning and robotics, with a particular focus on bridging the sim-to-real gap. His most-cited paper, "Analysing Deep Reinforcement Learning Agents Trained with Domain Randomisation" (2019, 7 citations), provides critical insights into how agents trained in simulation can successfully transfer to real-world environments. Dai systematically investigates the mechanisms behind domain randomisation, a popular method for achieving robust policy transfer, and offers a deeper understanding of why and how it works. This analysis is foundational for researchers aiming to deploy reinforcement learning in physical systems without requiring precise environmental models. By demystifying the inner workings of domain randomisation, Dai’s work helps guide the design of more effective training strategies for real-world robotic tasks. His contributions are particularly valuable for students and practitioners in robotics and AI who seek to develop agents that generalise beyond their training environments. Dai’s research continues to inform the development of reliable, simulation-trained policies for complex, unstructured settings.
Research Focus
Key Achievements
Top Papers
- 1