117 - Provably efficient reinforcement learning with Dr. Akshay Krishnamurthy

In this podcast episode, Dr. Akshay Krishnamurthy discusses the topic of reinforcement learning, a kind of machine learning that’s widely used for training agents to learn how to interact with their environment. He shares insights about feedback-driven data collection and provably efficient reinforcement learning algorithms that can make the RL algorithms more efficient.

Dr. Krishnamurthy is part of the Microsoft Research team based in New York City, which is at the forefront of reinforcement learning research. However, the goal of his research isn’t just to remain in the lab, but to move it into real-world applications. With his unique perspective and expertise in machine learning, he shares how a focus on efficient reinforcement learning algorithms can make a major breakthrough in the real-world application of machine learning.

This episode is a must-listen for anyone looking to stay up to date with the latest developments in machine learning research and their applications.

Listen to the full episode on Spotify: https://open.spotify.com/episode/6CvMeiHFsKZ3FJycetTzSE