Akshita Gupta

About

Akshita Gupta’s research lies at the critical intersection of reinforcement learning, control theory, and formal safety verification. Her most-cited work, “Safety Verification of Model Based Reinforcement Learning Controllers” (2020), tackles a fundamental challenge in deploying RL to real-world systems like robotics and autonomous driving: ensuring that learned controllers respect hard state-space constraints. Gupta’s key contribution is a rigorous framework for verifying the safety of model-based RL controllers before deployment, bridging the gap between data-driven learning and formal guarantees. This work has garnered 2 citations, establishing her as a rising voice in safe AI. Beyond this paper, her research agenda focuses on developing provably safe learning-based control methods, aiming to make autonomous systems both intelligent and trustworthy. For students and researchers, Gupta’s work is a compelling example of how formal methods can be integrated with modern machine learning to solve pressing safety-critical problems—a direction that is increasingly vital as RL moves from simulation to the real world.

Research Focus

Key Achievements

Top Papers

1. 1
   Safety Verification of Model Based Reinforcement Learning Controllers

   2 citations · 2020

Key Collaborators

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