Krutika Arvind Tomanvar
Papers
1
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1
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About
Krutika Arvind Tomanvar is a researcher at the intersection of machine learning and intelligent systems, with a primary focus on reinforcement learning (RL) and its practical applications. Her most cited work, "Reinforcement Learning and its application in making Recommendation System" (2023), explores how RL agents, grounded in the Markov Decision Process (MDP) framework, can optimize decision-making to maximize cumulative rewards. By bridging Q-learning with recommendation engines, she demonstrates how autonomous agents can learn from user interactions to deliver increasingly personalized content. Though early in her career, Tomanvar’s contributions are notable for translating foundational RL theory—such as the balance between exploration and exploitation—into actionable system designs. Her work highlights the potential of RL to transform recommendation systems from static filters into adaptive, reward-driven platforms. As her citation count grows, Tomanvar’s research promises to influence both the academic understanding of MDP-based learning and the development of more responsive, user-centric AI applications.
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