Smriti Gupta
Papers
1
Total Citations
4
H-Index
1
About
Smriti Gupta is a researcher at the forefront of reinforcement learning, with a focused interest in optimizing the interplay between algorithmic parameters for more efficient autonomous systems. Her most cited work, "Coupling Effect of Exploration Rate and Learning Rate for Optimized Scaled Reinforcement Learning" (2023), introduces a novel framework that systematically tunes the balance between exploration and learning speed. This contribution is critical for scaling RL algorithms to complex, real-world environments where computational efficiency is paramount. Although early in her career, her paper has already garnered 4 citations, signaling growing recognition within the AI community. Gupta’s research addresses a fundamental bottleneck in machine learning—how to make agents learn faster without sacrificing the breadth of their exploration. Her work holds promise for applications in robotics, autonomous navigation, and adaptive control systems. As she continues to refine these coupling dynamics, Smriti Gupta is establishing herself as a thoughtful contributor to the next generation of scalable, intelligent decision-making systems.
Research Focus
Key Achievements
Top Papers
- 1