Navigating the Challenges of Reinforcement Learning

By Siddhartha Chattaraj

In a recent guest lecture, Mr. Vishal Kumar, a seasoned Data Science Manager at American Express, delved into the intricacies of reinforcement learning, focusing on the nuances of open-time optimization and the challenges associated with long-term rewards. Under the guidance of Dr. Ajey Kumar, mentor of the Guest Lecture Committee for the Data Science and Data Analytics division, the lecture provided insights into real-life scenarios. Mr. Vishal Kumar shed light on the significance of distinguishing between exploitation and exploration using a relatable example from the casinos.

In a captivating introduction by Pujali Bhaumik, attendees were welcomed to a thought-provoking guest lecture featuring Mr. Vishal Kumar, where he elucidated the complexities of tackling long-term rewards in reinforcement learning, emphasizing the need for a comprehensive understanding of the trade-off between exploiting known strategies and exploring new possibilities. To address this challenge, he introduced two prominent approaches: greedy decision-making and Thompson sampling. The former involves selecting optimal actions, while the latter incorporates a probabilistic strategy, allowing for a balance between exploration and exploitation.

Transitioning seamlessly from theory to practical application, Kumar presented a compelling problem statement. He outlined the objectives and intricacies of the task, providing insights into the crucial steps of data preparation, data weightage, and updating parameters. The lecture culminated in a demonstration of real-life Python code, showcasing the implementation of the discussed concepts in a tangible and applicable manner.

Mr. Vishal Kumar’s expertise in the field and his ability to bridge theoretical concepts with practical applications gave attendees valuable insights into reinforcement learning. The lecture not only shed light on the challenges of long-term rewards but also equipped the audience with actionable strategies to navigate and overcome them in their own data science endeavors. As the representative from the guest lecture committee, Prachi Mishra provided a closing perspective, leaving the audience inspired and well-equipped to navigate the challenges of reinforcement learning in their data science pursuits.