RLlib on Ray is an industrial-strength reinforcement learning (RL) framework with the power of Ray autoscaling built in.
Get an overview of RLlib, and learn why organizations like Wildlife Studios and Dow Chemicals are using it to apply RL to business problems like recommendations and supply chain optimization.
This webinar will also show how to set up an environment, train a model, and deploy to an HTTPS endpoint to serve your policy in production.
Will is a Product Manager for ML at Anyscale. Previously, he was the first ML Engineer at Coinbase, and ran a couple of ML-related startups, one in the data labeling space and the other in the pharmaceutical space. He has a BS in CS and Music Composition from MIT, and did his master's thesis at MIT in machine learning systems. In his spare time, he produces electronic music, travels, and tries to find the best Ethiopian food in the Bay Area.
Sven has been working as a machine learning engineer for Anyscale Inc. since early 2020. He is the lead developer of "RLlib", Ray's industry-grade, scalable reinforcement learning (RL) library. His team is currently focusing on better supporting the most promising industry use cases, such as massive-multi-agent algorithms for league-based self-play, working with recommender systems and slate recommendation algos, such as contextual bandits, as well as, integrating with Ray's new datasets library for a better offline RL experience. A continuing effort of his is asserting high levels of stability and test coverage to ensure RLlib's rapid adoption in industry and research and helping to grow its community and contributor base. Before starting at Anyscale, he has been a leading developer of other successful open-source RL library projects, such as "RLgraph" and "TensorForce".