Papers
3
Total Citations
110
H-Index
3
About
Jiexin Wang is a researcher whose work lies at the intersection of reinforcement learning, robotics, and bio-inspired control systems. Their primary research areas include modular deep reinforcement learning, policy search algorithms, and robot navigation. Wang’s most notable contribution is the development of a modular deep reinforcement learning framework for robot navigation, which decomposes complex monolithic tasks into parallel sub-goals—a pattern inspired by neural evidence from animal brains. This work, published in 2020, has garnered 60 citations, highlighting its influence in the field. Additionally, Wang has advanced EM-based policy search methods, demonstrating their effectiveness in real-world robotic tasks such as the view-based positioning of a smartphone balancer (41 citations) and the standing and balancing of a two-wheeled smartphone robot. Their 2016 paper on EM-based Policy Hyper Parameter Exploration (EPHE) integrates Policy Gradient with Parameter Exploration and EM-based Reward-Weighted Regression, offering a gradient-free, stable learning approach. Through these contributions, Wang has pushed the boundaries of how robots learn from reward and punishment, making strides toward more adaptive, animal-like learning in autonomous systems.
Research Focus
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