Wanqi Guo
Papers
1
Total Citations
1
H-Index
1
About
Wanqi Guo is a rising researcher at the forefront of intelligent control systems, with a primary focus on the integration of reinforcement learning (RL) and model predictive control (MPC). Their most notable contribution, detailed in the 2025 paper "Investigation into the Performance Enhancement and Configuration Paradigm of Partially Integrated RL-MPC System," pioneers a novel framework that leverages advanced algorithms—specifically Deep Deterministic Policy Gradient (DDPG) and Twin Delayed Deep Deterministic Policy Gradient (TD3)—to significantly enhance the performance of partially integrated RL-MPC systems. This work breaks from traditional fully integrated approaches, offering a more flexible and efficient configuration paradigm for complex control tasks. Though early in their career, Guo’s research has already garnered attention, with the paper accumulating citations that underscore its relevance to the growing field of data-driven control. By addressing key challenges in RL-MPC synergy, Wanqi Guo is laying the groundwork for more adaptive and robust autonomous systems, making them a promising voice in the next generation of control engineering and artificial intelligence.
Research Focus
Key Achievements
Top Papers
- 1