Highly-scalable RL Library for Real-world Applications

In reinforcement learning (RL), an agent learns how to optimize performance solely by collecting experience in the real world or via a simulator. RL is being applied to problems such as decision making, process optimization (e.g., manufacturing and supply chains), ad serving, recommendations, self-driving cars, and algorithmic trading.
In this talk, I will discuss RLlib, a reinforcement learning library built on Ray with a strong focus on large-scale execution and scalability, ease-of-use for general users, as well as customizability for developers and researchers.
RLlib offers autonomous task-learning via many common RL algorithms and it scales from a laptop to a cluster with hundreds of machines. It is used by dozens of organizations, from startups to research labs to large organizations. You will see RLlib in action with a live demo.
Dean Wampler

Dean is an expert in streaming data systems, focusing on applications of machine learning and artificial intelligence (ML/AI). He is Head of Develop Relations at Anyscale.com, which develops Ray, a system for scaling applications from a laptop to a cluster with ease. Previously, he was an engineering VP at Lightbend. He is a contributor to several open source projects, a frequent conference speaker and tutorial teacher. He has a Ph.D. in Physics from the University of Washington
  • Date: May 11, 10:00 (US Pacific Time)
  • Fee: Free
  • Available Seats: 0 (max 300)
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