Marlos C. Machado

I am 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, and real-world applications of all the above.
[Google Scholar] [DBLP]


Prospective students, click here.

Marlos C. Machado

Recent News

    • Marc 2o23: Our recent paper is now available on arXiv.
      • Loss of Plasticity in Continual Deep Reinforcement Learning (w/ Abbas, Zhao, Modayil, & White).
    • Feb. 2023: We had a paper accepted at JMLR.
      • Temporal Abstraction in Reinforcement Learning with the Successor Representation (w/ Barreto, Precup, & Bowling).
    • Jan. 2023: Our recent paper is now available on arXiv.
      • Deep Laplacian-based Options for TemporallyExtended Exploration (w/ Klissarov).
    • Jan. 2023: Our recent paper is now available on arXiv.
      • Trajectory-Aware Eligibility Traces for Off-Policy Reinforcement Learning (w/ Daley, White, & Amato).
    • Nov. 2022: I gave a talk as invited keynote at the 11th Brazilian Conference on Intelligent Systems (BRACIS).
      • Temporal Abstraction in RL with the Successor Representation.
    • Nov. 2022: Our recent paper is now available on arXiv.
      • Agent-State Construction with Auxiliary Inputs (w/ Tao, & White).
    • Aug. 2022: Erfan Miahi and David Tao successfully defended their M.Sc. theses!
    • May 2022: We had a paper accepted at UAI’22.

      • Temporal Abstractions-Augmented Temporally Contrastive Learning: An Alternative to the Laplacian in RL (w/ Erraqabi, Zhao, Sukhbaatar, Lazaric, Denoyer, & Bengio).

    • Mar. 2022: Our paper was nominated for the best paper award at AISTATS’22!
      • A General Class of Surrogate Functions for Stable and Efficient Reinforcement Learning (w/ Vaswani, Bachem, Totaro, Mueller, Garg, Geist, Castro, & Le Roux).
    • Mar. 2022: Our recent paper is now available on arXiv.
      • Investigating the Properties of Neural Network Representations in Reinforcement Learning (w/ Wang, Miahi, White, Abbas, Kumaraswamy, Liu,  & White).
    • Feb. 2022: Our recent paper is now available on arXiv.
      • Reward-Respecting Subtasks for Model-Based Reinforcement Learning (w/ Sutton, Holland, Szepesvari, Timbers, Tanner, & White).
    • Jan. 2022: We had a paper accepted at AISTATS’22.
      • A General Class of Surrogate Functions for Stable and Efficient Reinforcement Learning (w/ Vaswani, Bachem, Totaro, Mueller, Garg, Geist, Castro, & Le Roux).
    • Oct. 2021: Our recent paper is now available on arXiv.
      • Temporal Abstraction in Reinforcement Learning with the Successor Representation (w/ Barreto & Precup).
    • Oct. 2021: I gave a talk and participated in a panel at the Microsoft Summit Workshop on RL, Forwards and Backwards: Insights from Neuroscience.
      • Temporal Abstraction in RL with the Successor Representation.
    • 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).

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

Hello, world!