Mayuresh Bhovardhan
Papers
1
Total Citations
1
H-Index
1
About
Mayuresh Bhovardhan is an emerging researcher in the field of machine learning, with a focused interest in reinforcement learning (RL) and its practical applications, particularly within recommendation systems. His work explores how intelligent agents can be trained to make optimal decisions in dynamic environments by leveraging the Markov Decision Process (MDP) framework and Q-learning algorithms. By integrating RL into recommendation engines, Bhovardhan addresses the challenge of maximizing cumulative user reward, offering a more adaptive and personalized approach compared to traditional methods. His key contribution lies in demonstrating how RL can transform static recommendation systems into dynamic, learning-based models that continuously improve user engagement. While his most-cited paper, "Reinforcement Learning and its application in making Recommendation System" (2023), has garnered 1 citation, it represents a foundational step in applying sequential decision-making to real-world AI systems. Bhovardhan’s work is particularly relevant for students and researchers interested in the intersection of reinforcement learning and practical AI deployment, highlighting the potential for RL to revolutionize user-centric technologies.
Research Focus
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Top Papers
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