Bonsai Sets State-of-the-Art Reinforcement Learning Benchmark for Programming Industrial Control Systems
(Thomson Reuters ONE) -
AI Platform Builds on DeepMind's Work to Demonstrate Complex Grasp and Stack
Robotics Tasks With 45x Faster Training Times
BERKELEY, Calif., Sept. 19, 2017 (GLOBE NEWSWIRE) -- Bonsai, provider of an AI
platform that empowers enterprises to build and deploy intelligent systems,
today announced that its AI Platform established a new benchmark for programming
industrial control systems. Using a robotics task to demonstrate the
achievement, the platform successfully trained a simulated robotic arm to grasp
and stack blocks on top of one another by breaking down the task into simpler
sub-concepts. Bonsai's unique concept networking technique trains each sub-
concept separately, then trains the overall "grasp and stack" task 45x faster
than a comparable approach from Google's DeepMind. This feat demonstrates the
flexibility and efficiency of the platform for programming intelligent control
into a range of robotics, manufacturing, HVAC, and other industrial control
systems.
"Building off the foundation established by DeepMind, we were able to achieve
these results by combining state-of-the-art reinforcement learning techniques
with innovative features that are unique to the Bonsai Platform," said Marcos
Campos, Head of AI, Bonsai. "Using Bonsai, enterprises now have access to the
tools and technology to program control systems more efficiently than any other
commercially available reinforcement learning platform."
Key features of the Bonsai Platform enabling the achievement of this benchmark
include:
* Concept Networks allow developers to decompose complex tasks into smaller,
individual actions. In this robotic control demonstration, the task was
decomposed into a concept network of five subconcepts - reach for the
object, orient the hand for grasping the object, grasp the object, move the
object, and stack the object. The Platform first trained the system to learn
the concepts grasp, orient and stack using reinforcement learning. The
Platform then learned a meta-controller - or selector - concept to combine
the newly trained concepts with existing move and reach classical
controllers into a complete stacking solution. Bonsai's method of assembling
the sub-concepts successfully solved the entire task, and was 45x faster
than DeepMind's approach for leveraging sub-tasks in a similar setting.
* Gears is a new interoperability feature released this past June allowing
developers to leverage existing knowledge by integrating existing models
into the Bonsai Platform. The robotic arm used in this task was pre-
programmed with classical controllers for two concepts - move and reach. In
this task, Gears enables the blending of classical robotics controllers
for move and reach with the Bonsai trained neural networks for grasp,
orient and stack. The ability to combine these models provides an
organization far greater flexibility in programming robotic control systems.
* Reinforcement Learning trains an AI model by rewarding it on the actions it
takes in an environment. In this demonstration, the Bonsai Platform employed
Hierarchical Reinforcement Learning (HRL) to benefit from multiple levels of
decision making. HRL allows the Platform to train each individual concept to
solve a single task, and then train the system to combine the concepts to
deliver an end solution. In this demonstration, by combining concept
networks within HRL, the system is able to solve the ultimate task orders of
magnitude faster than alternate approaches.
Bonsai is the only commercially available solution that packages together these
techniques within a single platform. To apply for Bonsai's Early Access Program,
visit https://bons.ai/getting-started.
Resources
* Learn how the Bonsai AI Platform achieved state-of-the-art robotic
control: https://bons.ai/blog/robotics-blog
* Watch a video of the "grasp and stack" robotics task, enabled by
Bonsai: http://bns.ai/robotics_video
* Read our research paper about concept networks and hierarchical deep
reinforcement learning: http://bns.ai/drl_paper
About Bonsai
Bonsai offers an AI platform that empowers enterprises to build and deploy
intelligent systems. By completely automating the management of complex machine
learning libraries and algorithms, Bonsai enables enterprises to program AI
models that improve system control and enhance real-time decision support.
Businesses use these models today to increase automation and improve operational
efficiency of industrial systems including robotics, manufacturing, supply
chain, logistics, energy and utilities. Based in Berkeley, CA, Bonsai is backed
by leading investors including NEA, Microsoft Ventures, ABB, Samsung NEXT and
Siemens. To learn more, please visit: https://bons.ai/ or follow on Twitter
(at)BonsaiAI.
Media and Analyst Contact:
Bridget Hickey
bridget.hickey(at)bons.ai
+1 (628) 400-9072
This announcement is distributed by Nasdaq Corporate Solutions on behalf of Nasdaq Corporate Solutions clients.
The issuer of this announcement warrants that they are solely responsible for the content, accuracy and originality of the information contained therein.
Source: Bonsai via GlobeNewswire
Datum: 19.09.2017 - 17:37 Uhr
Sprache: Deutsch
News-ID 560497
Anzahl Zeichen: 5888
contact information:
Town:
Berkeley
Kategorie:
Business News
Diese Pressemitteilung wurde bisher 354 mal aufgerufen.
Die Pressemitteilung mit dem Titel:
"Bonsai Sets State-of-the-Art Reinforcement Learning Benchmark for Programming Industrial Control Systems"
steht unter der journalistisch-redaktionellen Verantwortung von
Bonsai (Nachricht senden)
Beachten Sie bitte die weiteren Informationen zum Haftungsauschluß (gemäß TMG - TeleMedianGesetz) und dem Datenschutz (gemäß der DSGVO).
