Marlos C. Machado

I am a research scientist at DeepMind, an adjunct professor at the University of Alberta, and a Canada CIFAR AI Chair through Amii. My research interests lie broadly in machine learning, specifically in reinforcement learning, representation learning, optimization, and real-world applications of all the above.
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Marlos C. Machado

News

    • Jun. 2021: I’ve been appointed Canada CIFAR AI Chair.
    • Jun. 2021: I’ve been appointed Amii Fellow.
    • May 2021: We had a paper accepted at ICML’21.
      • Beyond Variance Reduction: Understanding the True Impact of Baselines on Policy Optimization (w/ Chung*, Thomas*, & Le Roux).
    • Jan. 2021: We had a paper accepted at ICLR’21.
      • Contrastive Behavioral Similarity Embeddings for Generalization in Reinforcement Learning (w/ Agarwal, Castro, & Bellemare).
    • Jan. 2021: I started as a Research Scientist at DeepMind.
    • Jan. 2021: I became an adjunct professor at the University of Alberta.
    • Dec. 2020: We had a paper accepted at Nature.
      • Autonomous Navigation of Stratospheric Balloons using Reinforcement Learning (w/ Bellemare, Candido, Castro, Gong, Moitra, Ponda, & Wang).
    • Sep. 2020: We had a paper accepted at NeurIPS’20.
      • An Operator View of Policy Gradient Methods (w/ Ghosh & Le Roux).
    • Feb. 2020: I gave a talk at Stanford University.
      • Temporal Abstraction in RL with the Successor Representation.
    • Feb. 2020: I attended AAAI’20 in New York City.
    • Dec. 2019: We had two papers accepted at ICLR’20.
      • Exploration in Reinforcement Learning with Deep Covering Options (w/ Jinnai, Park, & Konidaris).
      • On Bonus Based Exploration Methods In The Arcade Learning Environment  (w/ Taiga, Fedus, Courville, & Bellemare).
    • Nov. 2019: We had a paper accepted at AAAI’20.
      • Count-Based Exploration with the Successor Representation (w/ Bellemare & Bowling).

Blog Posts

Different behaviours for baselines that induce the same variance

The True Impact of Baselines in Policy Gradient Methods

Looking at Policy Gradient Methods from an Operators Perspective

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