Mohak Sharma
Papers
1
Total Citations
1
H-Index
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About
Mohak Sharma is a researcher at the forefront of applying reinforcement learning (RL) to real-world systems, with a particular focus on recommendation engines. His work bridges the gap between foundational RL theory—grounded in Markov Decision Processes and Q-learning—and practical implementation, demonstrating how intelligent agents can be trained to optimize user engagement and content delivery. His most-cited paper, "Reinforcement Learning and its application in making Recommendation System" (2023), provides a clear, accessible framework for integrating RL into recommendation pipelines, showing how agents can learn to maximize cumulative rewards in dynamic environments. While his citation count is still growing, Sharma’s contribution lies in making complex RL concepts actionable for practitioners, offering a blueprint for building adaptive, reward-driven recommendation systems. His research is particularly valuable for students and engineers looking to move beyond static models and into interactive, learning-based systems. As the field of RL-driven personalization expands, Sharma’s work serves as a key entry point for understanding how autonomous agents can transform user experiences.
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Top Papers
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