With over 20 years as a global leader in AI, Amii is the trusted partner for helping businesses turn Reinforcement Learning (RL) into real business advantage. Read on to find out how.
RL is a type of AI that learns through experience, rather than through direct programming or supervision. That means building smarter, more resilient systems that adapt as conditions change.
How does a baby or an animal learn about the world? By trying things out, noticing the result, and trying again. RL works in a similar way.
Most AI models focus on classification, but RL goes further, learning strategies for sequential decision-making.
Instead of relying on fixed datasets, RL interacts with its environment, takes actions, and learns from feedback to improve over time.
RL can help your business:
Make smarter decisions in real time
Build systems that learn and improve
Turn uncertainty into opportunity
Watch: Amii CEO Cam Linke introduces Amii and what it can do for your business.
At Amii, we don’t just understand RL. We’ve been shaping it since day one. Our world-renowned RL experts are the reason leading companies trust us to develop complex AI theory into business-driving reality.
Turing Award Winner
A recent winner of the A.M. Turing Award, Richard Sutton is Amii's Chief Scientific Advisor. He is the co-author of Reinforcement Learning: An Introduction (1998, 2018), the foundational textbook that has shaped the field and continues to guide RL researchers today.
Professor Sutton is widely regarded as the world's leading expert in RL.
Amii Fellow
Michael Bowling is an Amii Fellow, a professor at the University of Alberta, and a Canada CIFAR AI Chair. He is also a principal investigator in the Reinforcement Learning and Artificial Intelligence (RLAI) Lab and leader of the Computer Poker Research Group.
Amii Fellow
Martha White is an assistant professor at the University of Alberta, an Amii Fellow, and a Canada CIFAR AI Chair. She is also the co-founder of RL Core Technologies, which uses RL to help manage wastewater treatment. She is known for working to create systems that can learn and adapt in real time, helping build more capable and intelligent autonomous agents.
Amii Fellow
Adam White is an Amii Fellow, Canada CIFAR AI Chair, and a Professor in the University of Alberta’s Department of Computing Science, where he is also Principal Investigator in the Reinforcement Learning & Artificial Intelligence (RLAI) Lab. He is also a co-founder of RL Core Technologies.
Amii Fellow
Patrick Pilarski is an Amii Fellow, Canada CIFAR AI Chair, and a Professor in the Department of Medicine, University of Alberta. An expert in limb prosthesis control using AI/RL, he is also co-principal investigator at BLINC Lab at the University of Alberta, as well as a Research Affiliate at the Glenrose Rehabilitation Hospital in Edmonton.
Amii Fellow
Csaba Szepesvári is an Amii Fellow, Canada CIFAR AI Chair, and professor in the Computing Science Department at the University of Alberta. He is also a Senior Staff Research Scientist at DeepMind, leading the Foundations team.
Amii Fellow
Dale Schuurmans is a Fellow and Canada CIFAR AI Chair at Amii, a Professor in the Department of Computing Science at the University of Alberta and a Senior Staff Research Scientist at Google Brain. He has supervised more than 50 early-career researchers at the M.Sc and Ph.D. levels.
Our team of research scientists works on RL projects with industry partners every day – making impacts that simply couldn't happen without the use of AI.
We worked with a multinational energy and petrochemical company to research and develop data-driven control solutions for large-scale chemical processes where a physical model was not available or too difficult or expensive to develop.
We worked with a water treatment technology provider to optimize membrane cleaning schedules in water treatment plants, reduce energy and chemical usage, improve operational efficiency, extend equipment lifespan, and improve water quality management overall.
We worked with a production optimization company in the energy sector to develop autonomous well control and efficiency solutions for upstream oil and gas producers, optimizing plunger lift operation set points.
We worked with a wastewater treatment operator on the deployment of modular water treatment systems into currently underserved regions, in order to ensure efficient and resilient deployment of water treatment to rural and remote areas.
We worked with a robotics company on a 3D warehouse automated storage and retrieval system. Vertical robots and a modular cube-based structure drastically reduced warehouse space and improved order fulfillment speed, maximizing throughput and item allocation.
In this project, we used RL to optimize component sizing as well as other operating conditions to meet specific requirements for circuit design.
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