What does the content in your YouTube queue have in common with supply chain optimization and financial market simulation? All of these represent complex problems that are being solved today with reinforcement learning (RL).
On March 29, we’re bringing together machine learning engineers, data scientists, and researchers who are pioneering this exciting area for a full day of talks, hands-on learning, and networking: the first-ever Production RL Summit.
The Summit has something for everyone, from novices to experts. Register for the event now (it’s free and virtual!), or read on for five reasons why you might want to attend.
Maybe you’re a data science student who is interested in learning more about applied RL and how it’s being used across a variety of industries. Or, maybe you’re a machine learning engineer or data scientist who is interested in bringing RL into your AI strategy. Even if you’re not an RL expert, we have a diverse group of featured speakers from J.P. Morgan, Siemens, and more who will give you the background you need to start exploring how to bring RL into your work.
Why don’t we just use RL instead of traditional machine learning to solve all of our decision-making problems? One reason is that modern RL does not effectively utilize the kinds of large and highly diverse datasets that have been instrumental to the success of supervised machine learning. During the opening keynote, Sergey Levine from UC Berkeley will explain how offline RL can be used to address this limitation.
We also have an inspiring lineup of featured speakers who will be sharing their RL success stories, such as J.P. Morgan’s Sumitra Ganesh, who will be speaking on how her team is using RL to solve some of the biggest challenges in agent-based modeling to simulate financial markets, and Adam Kelloway from Dow, Inc., who will share how he has been able to deploy advanced RL-based agents into a legacy industrial setting.
The potential of RL has long been recognized by academics and analysts, but practical applications for enterprises have been slower to materialize. In 2020, Gartner noted that RL “is still limited in its enterprise deployments, but its superior precision and targeting is promising for the future.”
But the reality is that RL is already being used in production across a variety of industries. Featured speaker Ben Kasper, staff data scientist at Riot Games, will share how RL is having a real impact in the gaming industry: Ben’s team is using RL to analyze and iterate on game balance and improve the game experience for players.
Even in industrial domains such as steel, paper, and power plants, there are a number of RL applications that likely impact your everyday life. Be sure to check out the session with Volkmar Sterzing and Dr. Marc Weber from Siemens Technology for a discussion of RL use cases in the physical world, starting with RL-controlled power plant gas turbines.
If you’re an RL practitioner who wants to connect with and learn from industry experts and peers, you’re in luck: immediately following the featured sessions, we’ll be holding a 1-hour networking session where you’ll have the opportunity to connect and share with your peers and experts in the RL community. Ask questions, share learnings, and build your network.
Armed with success stories, real-world applications, and a larger professional network, you’ll be ready to build your practical RL skills. That’s why we’re rounding out the day with a hands-on tutorial on how to apply cutting-edge RL techniques to build a recommender system with RLlib. We’ll cover techniques including multi-armed bandits in OpenAI Gym and Google’s latest SlateQ algorithm in their RecSim recommender environment.
Check out the Production RL Summit website for more details on the tutorial, including how to purchase a tutorial pass.
If you have questions about the event, feel free to reach out to us at firstname.lastname@example.org. Excited about the event? Share why using the hashtag #ProdRLSummit!
We look forward to seeing you at the Production RL Summit on March 29!