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8-9 Oct 2018: Training
9-11 Oct 2018: Tutorials & Conference
London, UK

Building reinforcement learning applications with Ray

Ion Stoica (UC Berkeley), Robert Nishihara (UC Berkeley), Philipp Moritz (UC Berkeley), Richard Liaw (UC Berkeley RISELab), Eric Liang (UC Berkeley RISELab)
13:30–17:00 Tuesday, 9 October 2018
Location: Blenheim Room - Palace Suite
Secondary topics:  Reinforcement Learning, Text, Language, and Speech

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


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 Ion Stoica

Ion Stoica

UC 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).

Photo of Robert Nishihara

Robert Nishihara

UC 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

UC 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 Richard Liaw

Richard Liaw

UC Berkeley RISELab

Richard Liaw is a PhD student in BAIR/RISELab at UC Berkeley working with Joseph Gonzalez, Ion Stoica, and Ken Goldberg. He has worked on a variety of different areas, ranging from robotics to reinforcement learning to distributed systems. He is currently actively working on Ray, a distributed execution engine for AI applications; RLlib, a scalable reinforcement learning library; and Tune, a distributed framework for model training.

Photo of Eric Liang

Eric Liang

UC Berkeley RISELab

Eric Liang is a second-year PhD student working in the UC Berkeley RISELab with Ion Stoica. He works on frameworks and applications for machine learning and reinforcement learning. Before Berkeley, he spent several years working on systems in industry at Databricks and Google.

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