Marlos Machado

Marlos C. Machado

Fellow & Canada CIFAR AI Chair

Academic Affiliations

Adjunct Professor – University of Alberta (Computing Science)

Industry and Research Affiliations

Research Scientist – DeepMind

Areas of Expertise

Artificial intelligence; machine learning; reinforcement learning; representation learning; optimization; generalization; exploration; real-world applications

Marlos designs algorithms that learn abstractions for better credit assignment, generalization, and exploration in reinforcement learning.

Representation and 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 Fellow and Canada CIFAR AI Chair at Amii, a research scientist at DeepMind, and an adjunct professor at the University of Alberta. He received his B.Sc. and M.Sc. from Universidade Federal de Minas Gerais, in Brazil, and his Ph.D. from the University of Alberta, where he introduced the idea of temporally-extended exploration through options. He was a research scientist at Google Brain from 2019 to 2021, during which time he made major contributions to reinforcement learning, in particular the introduction of an operator view of policy gradient methods and the application of deep reinforcement learning to control Loon's stratospheric balloons. During his graduate studies, Marlos also had experience working at Microsoft Research and IBM Research. 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.

Featured Articles

Marlos’s work has been published in the leading conferences and journals in machine learning, including Nature, JMLR, JAIR, NeurIPS, ICML and ICLR.

Articles by Marlos C. Machado

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