Presented By O’Reilly and Intel Nervana
Put AI to work
September 17-18, 2017: Training
September 18-20, 2017: Tutorials & Conference
San Francisco, CA

Building reinforcement learning applications with Ray

Ion Stoica (UC Berkeley), Robert Nishihara (UC Berkeley), Philipp Moritz (UC Berkeley)
1:30pm–5:00pm Monday, September 18, 2017
Implementing AI
Location: Imperial A Level: Intermediate
Secondary topics:  Algorithms, Open source, Transportation and autonomous vehicles
Average rating: ****.
(4.57, 7 ratings)

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 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.

Comments on this page are now closed.

Comments

Tom Duffy | SILICON ENGINEERING MANAGER
09/29/2017 2:56pm PDT

Thanks!

Picture of Robert Nishihara
Robert Nishihara | GRADUATE STUDENT
09/29/2017 2:42pm PDT

You can find most of the material at https://github.com/ray-project/tutorial.

Tom Duffy | SILICON ENGINEERING MANAGER
09/29/2017 2:39pm PDT

Can I download the training material from this class? The notebooks.