Marlos C. Machado
Fellow & Canada CIFAR AI Chair
Academic Affiliations
Industry and Research Affiliations
Areas of Expertise
Fellow & Canada CIFAR AI Chair
Academic Affiliations
Industry and Research Affiliations
Areas of Expertise
Marlos designs algorithms that learn abstractions for better credit assignment, generalization, and exploration in reinforcement learning.
Through his research, Marlos seeks to develop reinforcement learning methods that can be meaningfully used in real-world settings. He focuses on designing algorithms capable of learning abstractions that allow AI agents to tackle the three fundamental problems of reinforcement learning: generalization, exploration and credit-assignment. Currently, he is focused on designing theoretically-grounded algorithms that tackle these three problems concurrently. His research includes a number of different avenues, including designing representation learning methods tailored for reinforcement learning problems, developing AI agents that are capable of discovering temporally extended-behaviors (known as options), and creating systems capable of continual learning. Marlos is also passionate about reproducibility and proper experimentation in machine learning, having led several efforts on this topic in the past.
Marlos is a Fellow in Residence and Canada CIFAR AI Chair at the Alberta Machine Intelligence Institute (Amii), and an adjunct professor at the University of Alberta. Marlos’s research mostly focuses on the problem of reinforcement learning. He received his B.Sc. and M.Sc. from UFMG, in Brazil, and his Ph.D. from the University of Alberta, where he popularized the idea of temporally-extended exploration through options. He was a researcher at DeepMind from 2021 to 2023 and at Google Brain from 2019 to 2021, during which time he made major contributions to reinforcement learning, in particular the application of deep reinforcement learning to control Loon’s stratospheric balloons.
Marlos’ work has been published in the leading conferences and journals in AI, including Nature, JMLR, JAIR, NeurIPS, ICML, ICLR, and AAAI. His research has also been featured in popular media such as BBC, Bloomberg TV, The Verge, and Wired.
Dec 1st 2020
Research Post
Jan 1st 2018
Research Post
Jul 6th 2021
Research Post
Marlos’s work has been published in the leading conferences and journals in machine learning, including Nature, JMLR, JAIR, NeurIPS, ICML and ICLR.
Jul 6th 2022
News
The June 2022 edition of AI Meetup was all about stratospheric balloons, featuring Amii’s Marlos C. Machado.
Jul 13th 2021
News
The work of Amii researchers is being featured at the 38th annual International Conference on Machine Learning (ICML), running online this year from July 18 to 24.
Jul 6th 2021
Research Post
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