Fengji Yi
Papers
1
Total Citations
19
H-Index
1
About
Fengji Yi is a researcher whose work bridges the frontiers of machine learning and artificial intelligence, with a particular focus on model-based reinforcement learning (MBRL). In their highly cited survey, "Model-based reinforcement learning: A survey" (2018), Yi provided a comprehensive overview of this rapidly evolving field, which distinguishes itself from traditional model-free approaches by learning a model of the environment to simulate outcomes and optimize policies more efficiently. This foundational work, earning 19 citations, has served as a key reference for researchers seeking to understand how MBRL can improve sample efficiency and decision-making in complex, dynamic systems. Yi’s contributions highlight the potential of integrating learned models with reinforcement learning algorithms, offering a pathway toward more intelligent and adaptive agents. By clarifying the strengths and challenges of MBRL, Yi has helped shape ongoing discussions in AI, making their work essential reading for students and researchers exploring advanced reinforcement learning techniques.
Research Focus
Key Achievements
Top Papers
- 1Model-based reinforcement learning: A survey19 citations · 2018