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Put AI to Work
April 29-30, 2018: Training
April 30-May 2, 2018: Tutorials & Conference
New York, NY

Building reinforcement learning applications with Ray

Robert Nishihara (University of California, Berkeley), Philipp Moritz (University of California, Berkeley), Ion Stoica (University of California, Berkeley)
1:40pm–5:10pm Monday, April 30, 2018
Implementing AI, Models and Methods
 Location: Regent Parlor
Average rating: ***..
(3.00, 1 rating)

Who is this presentation for?

  • Machine learning researchers and practitioners and data scientists who want to learn reinforcement learning and build applications

Prerequisite knowledge

  • Familiarity with Python programming, basic machine learning concepts, and reinforcement learning

Materials or downloads needed in advance

  • A laptop

What you'll learn

  • Learn how to develop simple RL applications at scale with Ray and manage Ray on clusters

Description

Reinforcement learning (RL) is emerging as a promising approach to intelligently interact with continuously changing physical or virtual environments. Advances in RL research have already shown remarkable results, such as Google’s AlphaGo beating the Go world champion, and are finding their way into self-driving cars, unmanned aerial vehicles, and surgical robotics. Not surprisingly, many see RL growing rapidly into a potentially dominant area in ML over the next decade. However, the applications of RL pose a new set of requirements, the combination of which creates a challenge for existing distributed execution frameworks: computation with millisecond latency at high throughput, adaptive construction of arbitrary task graphs, and execution of heterogeneous kernels over diverse sets of resources.

Ion Stoica, Robert Nishihara, and Philipp Moritz lead a deep dive into Ray, a new distributed execution framework for reinforcement learning applications developed by machine learning and systems researchers at UC Berkeley’s RISELab, walking you through Ray’s API and system architecture and sharing application examples, including several state-of-the art RL algorithms.

Photo of Robert Nishihara

Robert Nishihara

University of California, Berkeley

Robert Nishihara is a fourth-year PhD student working in the UC Berkeley RISELab with Michael Jordan. He works on machine learning, optimization, and artificial intelligence.

Photo of Philipp Moritz

Philipp Moritz

University of California, Berkeley

Philipp Moritz is a PhD candidate in EECS at UC Berkeley, with broad interests in artificial intelligence, machine learning, and distributed systems. He is a member of the Statistical AI Lab and the RISELab.

Photo of Ion Stoica

Ion Stoica

University of California, Berkeley

Ion Stoica is a professor in the EECS Department at the University of California, Berkeley, where he does research on cloud computing and networked computer systems. Ion’s previous work includes dynamic packet state, chord DHT, internet indirection infrastructure (i3), declarative networks, and large-scale systems, including Apache Spark, Apache Mesos, and Alluxio. He is the cofounder of Databricks—a startup to commercialize Apache Spark—and Conviva—a startup to commercialize technologies for large-scale video distribution. Ion is an ACM fellow and has received numerous awards, including inclusion in the SIGOPS Hall of Fame (2015), the SIGCOMM Test of Time Award (2011), and the ACM doctoral dissertation award (2001).