Learn and Adapt: Shaping AI with Reinforcement Learning using the Gymnasium Framework
Karan Jagtiani (~karan6) |
Description:
The Problem Statement: Reinforcement Learning (RL) often seems challenging and complex to newcomers, despite being a powerful machine learning paradigm that enables AI agents to learn from their own actions and decisions. This talk aims to demystify RL, providing a clear understanding of its origins, mechanisms, and practical implementation with the Gymnasium Framwork.
Who Should Attend: This talk is ideal for anyone interested in machine learning and AI, including but not limited to data scientists, AI enthusiasts, students studying computer science or related fields, and developers looking to expand their skills. Both beginners and those with some familiarity with machine learning will find the talk beneficial.
Why Should You Attend: By attending this talk, you will gain a solid understanding of Reinforcement Learning and its place in the AI landscape. The live demo using the Gymnasium Framework will give you practical knowledge about implementing RL in real-world scenarios. Furthermore, you will learn about RL's potential impact and future trends, providing you with insights beneficial to your professional development in AI and ML.
Brief Outline:
- Introduction to the Talk and Speaker (2 minutes)
- Origins of Artificial Intelligence & Transition to Machine Learning (2 minutes)
- The Need for a Different Learning Paradigm (2 minutes)
- Birth and Evolution of Reinforcement Learning (2 minutes)
- Exploring Reinforcement Learning (2 minutes)
- Introduction to the Gymnasium Framework (3 minutes)
- Live Demo: Building an RL Agent using the Gymnasium Framework (10 minutes)
- Concluding Remarks, Potential Applications, and Future Trends (2 minutes)
- Q&A (5 minutes)
Prerequisites:
To get the most out of this talk, attendees should have a foundational knowledge of Python programming and basic familiarity with machine learning concepts. Understanding of concepts like states, actions, and rewards, while not necessary, could be beneficial.
Video URL:
https://www.youtube.com/watch?v=3FQEFnCxLls&ab_channel=KaranJagtiani
Speaker Info:
Karan Jagtiani is a versatile Software Engineer with a demonstrated history of two years of professional expertise. Currently, he's working as an SDE 2 at HackerRank, where he harnesses his strong problem-solving skills to build innovative products and identify their market potential. Over the years, he has had the opportunity to work in various domains like Full Stack Development, DevOps, Computer Vision, IoT and much more.
In addition to his professional roles, Karan was the Founder & President of the Revolution Software Club at his university. He created this club to share knowledge about technologies beyond the standard curriculum, underlining his commitment to continuous learning and teaching. Over the course of a year, he conducted multiple live technical workshops for the community, videos for which can be found on this YouTube channel. Karan's background extends to academia as well, having earned a Bachelor's degree with a Gold medal in Computer Engineering.
From coding complex algorithms to managing his own software club, Karan has shown a deep dedication to his field. His diverse experience, passion for sharing knowledge, and experience with public speaking make him an excellent fit to discuss Reinforcement Learning and the Gymnasium Framework at the conference.