ICSE 2022 (series) / CAIN 2022 (series) / CAIN 2022 - 1st International Conference on AI Engineering - Software Engineering for AI / Engineering a Platform for Reinforcement Learning Workloads
Engineering a Platform for Reinforcement Learning WorkloadsIndustry Talk
Reinforcement Learning (RL) is an area of machine learning concerned with teaching intelligent agents to take desired actions in a specific environment. The teaching part can be performed in a simulated environment where the agent can learn how to react to the (simulated) current state in order to reach a desired state. Offering Reinforcement Learning as a service with stringent reliability and scalability requirements, entails a set of challenges at both the architectural and implementation level. In this paper we present the Bonsai platform for RL workloads. We discuss the requirements, design and implementation of the Bonsai platform.
Mon 16 MayDisplayed time zone: Eastern Time (US & Canada) change
Mon 16 May
Displayed time zone: Eastern Time (US & Canada) change
09:30 - 11:00 | Training & LearningCAIN 2022 at CAIN main room Chair(s): Jan Bosch Chalmers University of Technology | ||
09:30 15mResearch paper | An Empirical Evaluation of Flow Based Programming in the Machine Learning Deployment ContextResearch Paper CAIN 2022 Andrei Paleyes Department of Computer Science and Technology, Univesity of Cambridge, Christian Cabrera Department of Computer Science and Technology, Univesity of Cambridge, Neil D. Lawrence Department of Computer Science and Technology, Univesity of Cambridge Pre-print Media Attached | ||
09:45 15mResearch paper | Automatic Checkpointing and Deterministic Training for Deep LearningResearch Paper CAIN 2022 Xiangzhe Xu Purdue University, Hongyu Liu Huawei Galois Lab, China, Guanhong Tao Purdue University, USA, Zhou Xuan Purdue University, Xiangyu Zhang Purdue University | ||
10:00 15mResearch paper | Influence-Driven Data Poisoning in Graph-Based Semi-Supervised ClassifiersResearch Paper CAIN 2022 Adriano Franci University of Luxembourg, Maxime Cordy University of Luxembourg, Luxembourg, Martin Gubri University of Luxembourg, Luxembourg, Mike Papadakis University of Luxembourg, Luxembourg, Yves Le Traon University of Luxembourg, Luxembourg | ||
10:15 15mIndustry talk | Engineering a Platform for Reinforcement Learning WorkloadsIndustry Talk CAIN 2022 | ||
10:30 15mResearch paper | Method Cards for Prescriptive Machine-Learning TransparencyResearch Paper CAIN 2022 David Adkins Meta AI, Bilal Alsallakh Meta AI, Adeel Cheema Meta AI, Narine Kokhlikyan Meta AI, Emily McReynolds Meta AI, Pushkar Mishra Meta AI, Chavez Procope Meta AI, Jeremy Sawruk Meta AI, Erin Wang Meta AI, Polina Zvyagina Meta AI | ||
10:45 15mOther | Discussion on Training & Learning CAIN 2022 | ||
Information for Participants
Mon 16 May 2022 09:30 - 11:00 at CAIN main room - Training & Learning Chair(s): Jan Bosch
Info for room CAIN main room: