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ORCIDMarlos C. Machado
Marlos Cholodovskis Machado
Person information
- affiliation: Department of Computing Science, University of Alberta, Canada
- affiliation (former): Federal University of Minas Gerais, Belo Horizonte, Brazil
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2020 – today
- 2026
[i51]Alex Lewandowski, Marlos C. Machado, Dale Schuurmans:
Universal computation is intrinsic to language model decoding. CoRR abs/2601.08061 (2026)- [i50]Hon Tik Tse, Marlos C. Machado:
DROGO: Default Representation Objective via Graph Optimization in Reinforcement Learning. CoRR abs/2602.00403 (2026) - [i49]Dikshant Shehmar, Matthew Schlegel, Matthew E. Taylor, Marlos C. Machado:
Laplacian Representations for Decision-Time Planning. CoRR abs/2602.05031 (2026) - [i48]Siddarth Chandrasekar, Marlos C. Machado:
The Laplacian Keyboard: Beyond the Linear Span. CoRR abs/2602.07730 (2026) - [i47]Prabhat Nagarajan, Brett Daley, Martha White, Marlos C. Machado:
Accelerating Q-learning through Efficient Value-Sharing across Actions. CoRR abs/2606.29806 (2026) - 2025
- [c31]Marlos C. Machado:
Representation-driven Option Discovery in Reinforcement Learning. AAAI 2025: 28705 - [c30]Martin Klissarov, Mikael Henaff, Roberta Raileanu, Shagun Sodhani, Pascal Vincent, Amy Zhang, Pierre-Luc Bacon, Doina Precup, Marlos C. Machado, Pierluca D'Oro:
MaestroMotif: Skill Design from Artificial Intelligence Feedback. ICLR 2025 - [c29]Alex Lewandowski, Michal Bortkiewicz, Saurabh Kumar, András György, Dale Schuurmans, Mateusz Ostaszewski, Marlos C. Machado:
Learning Continually by Spectral Regularization. ICLR 2025 - [c28]Alex Lewandowski, Dale Schuurmans, Marlos C. Machado:
Plastic Learning with Deep Fourier Features. ICLR 2025 - [c27]Alex Lewandowski, Adtiya A. Ramesh, Edan Meyer, Dale Schuurmans, Marlos C. Machado:
The World Is Bigger! A Computationally-Embedded Perspective on the Big World Hypothesis. NeurIPS 2025 - [c26]Hon Tik Tse, Siddarth Chandrasekar, Marlos C. Machado:
Reward-Aware Proto-Representations in Reinforcement Learning. NeurIPS 2025 - [i46]Hon Tik Tse, Siddarth Chandrasekar, Marlos C. Machado:
Reward-Aware Proto-Representations in Reinforcement Learning. CoRR abs/2505.16217 (2025) - [i45]Mohamed A. Mohamed, Kateryna Nekhomiazh, Vedant Vyas, Marcos M. Jose, Andrew Patterson, Marlos C. Machado:
The Cell Must Go On: Agar.io for Continual Reinforcement Learning. CoRR abs/2505.18347 (2025) - [i44]Martin Klissarov, Akhil Bagaria, Ziyan Luo, George Dimitri Konidaris, Doina Precup, Marlos C. Machado:
Discovering Temporal Structure: An Overview of Hierarchical Reinforcement Learning. CoRR abs/2506.14045 (2025) - [i43]Prabhat Nagarajan, Martha White, Marlos C. Machado:
Double Q-learning for Value-based Deep Reinforcement Learning, Revisited. CoRR abs/2507.00275 (2025) - [i42]Esraa Elelimy, Brett Daley, Andrew Patterson, Marlos C. Machado, Adam White, Martha White:
Deep Reinforcement Learning with Gradient Eligibility Traces. CoRR abs/2507.09087 (2025) - [i41]Harshil Kotamreddy, Marlos C. Machado:
A Study of Value-Aware Eigenoptions. CoRR abs/2507.09127 (2025) - [i40]Brett Daley, Prabhat Nagarajan, Martha White, Marlos C. Machado:
An Analysis of Action-Value Temporal-Difference Methods That Learn State Values. CoRR abs/2507.09523 (2025) - [i39]Alex Lewandowski, Adtiya A. Ramesh, Edan Meyer, Dale Schuurmans, Marlos C. Machado:
The World Is Bigger! A Computationally-Embedded Perspective on the Big World Hypothesis. CoRR abs/2512.23419 (2025) - 2024
- [j12]Edan Meyer, Adam White, Marlos C. Machado:
Harnessing Discrete Representations for Continual Reinforcement Learning. RLJ 2: 606-628 (2024) - [j11]Brett Daley, Marlos C. Machado, Martha White:
Demystifying the Recency Heuristic in Temporal-Difference Learning. RLJ 3: 1019-1036 (2024) - [j10]Han Wang
, Erfan Miahi, Martha White, Marlos C. Machado, Zaheer Abbas, Raksha Kumaraswamy, Vincent Liu, Adam White:
Investigating the properties of neural network representations in reinforcement learning. Artif. Intell. 330: 104100 (2024) - [j9]Muhammad Kamran Janjua, Haseeb Shah, Martha White, Erfan Miahi, Marlos C. Machado, Adam White:
GVFs in the real world: making predictions online for water treatment. Mach. Learn. 113(8): 5151-5181 (2024) - [j8]Subhojeet Pramanik, Esraa Elelimy, Marlos C. Machado, Adam White:
AGaLiTe: Approximate Gated Linear Transformers for Online Reinforcement Learning. Trans. Mach. Learn. Res. 2024 (2024) - [c25]Richard S. Sutton, Marlos C. Machado, G. Zacharias Holland, David Szepesvari, Finbarr Timbers, Brian Tanner, Adam White:
Reward-Respecting Subtasks for Model-Based Reinforcement Learning (Abstract Reprint). AAAI 2024: 22713 - [c24]Diego Gomez, Michael Bowling, Marlos C. Machado:
Proper Laplacian Representation Learning. ICLR 2024 - [c23]Brett Daley, Martha White, Marlos C. Machado:
Averaging n-step Returns Reduces Variance in Reinforcement Learning. ICML 2024: 9904-9930 - [i38]Brett Daley, Martha White, Marlos C. Machado:
Compound Returns Reduce Variance in Reinforcement Learning. CoRR abs/2402.03903 (2024) - [i37]Alex Lewandowski, Saurabh Kumar, Dale Schuurmans, András György, Marlos C. Machado:
Learning Continually by Spectral Regularization. CoRR abs/2406.06811 (2024) - [i36]Brett Daley, Marlos C. Machado, Martha White:
Demystifying the Recency Heuristic in Temporal-Difference Learning. CoRR abs/2406.12284 (2024) - [i35]Alex Lewandowski, Dale Schuurmans, Marlos C. Machado:
Plastic Learning with Deep Fourier Features. CoRR abs/2410.20634 (2024) - [i34]Martin Klissarov, Mikael Henaff, Roberta Raileanu, Shagun Sodhani, Pascal Vincent, Amy Zhang, Pierre-Luc Bacon, Doina Precup, Marlos C. Machado, Pierluca D'Oro:
MaestroMotif: Skill Design from Artificial Intelligence Feedback. CoRR abs/2412.08542 (2024) - 2023
- [j7]Richard S. Sutton, Marlos C. Machado, G. Zacharias Holland, David Szepesvari, Finbarr Timbers, Brian Tanner, Adam White:
Reward-respecting subtasks for model-based reinforcement learning. Artif. Intell. 324: 104001 (2023) - [j6]Marlos C. Machado, André Barreto, Doina Precup, Michael Bowling:
Temporal Abstraction in Reinforcement Learning with the Successor Representation. J. Mach. Learn. Res. 24: 80:1-80:69 (2023) - [j5]Ruo Yu Tao, Adam White, Marlos C. Machado:
Agent-State Construction with Auxiliary Inputs. Trans. Mach. Learn. Res. 2023 (2023) - [c22]Zaheer Abbas, Rosie Zhao, Joseph Modayil, Adam White, Marlos C. Machado:
Loss of Plasticity in Continual Deep Reinforcement Learning. CoLLAs 2023: 620-636 - [c21]Brett Daley, Martha White, Christopher Amato, Marlos C. Machado:
Trajectory-Aware Eligibility Traces for Off-Policy Reinforcement Learning. ICML 2023: 6818-6835 - [c20]Martin Klissarov, Marlos C. Machado:
Deep Laplacian-based Options for Temporally-Extended Exploration. ICML 2023: 17198-17217 - [i33]Martin Klissarov, Marlos C. Machado:
Deep Laplacian-based Options for Temporally-Extended Exploration. CoRR abs/2301.11181 (2023) - [i32]Brett Daley, Martha White, Christopher Amato, Marlos C. Machado:
Trajectory-Aware Eligibility Traces for Off-Policy Reinforcement Learning. CoRR abs/2301.11321 (2023) - [i31]Zaheer Abbas, Rosie Zhao, Joseph Modayil, Adam White, Marlos C. Machado:
Loss of Plasticity in Continual Deep Reinforcement Learning. CoRR abs/2303.07507 (2023) - [i30]Diego Gomez, Michael Bowling, Marlos C. Machado:
Proper Laplacian Representation Learning. CoRR abs/2310.10833 (2023) - [i29]Subhojeet Pramanik, Esraa Elelimy, Marlos C. Machado, Adam White:
Recurrent Linear Transformers. CoRR abs/2310.15719 (2023) - [i28]Alex Lewandowski, Haruto Tanaka, Dale Schuurmans, Marlos C. Machado:
Curvature Explains Loss of Plasticity. CoRR abs/2312.00246 (2023) - [i27]Edan Meyer, Adam White, Marlos C. Machado:
Harnessing Discrete Representations For Continual Reinforcement Learning. CoRR abs/2312.01203 (2023) - [i26]Muhammad Kamran Janjua, Haseeb Shah, Martha White, Erfan Miahi, Marlos C. Machado, Adam White:
GVFs in the Real World: Making Predictions Online for Water Treatment. CoRR abs/2312.01624 (2023) - 2022
- [c19]Sharan Vaswani, Olivier Bachem, Simone Totaro, Robert Müller, Shivam Garg, Matthieu Geist, Marlos C. Machado, Pablo Samuel Castro, Nicolas Le Roux:
A general class of surrogate functions for stable and efficient reinforcement learning. AISTATS 2022: 8619-8649 - [c18]Akram Erraqabi, Marlos C. Machado, Mingde Zhao, Sainbayar Sukhbaatar, Alessandro Lazaric, Ludovic Denoyer, Yoshua Bengio:
Temporal abstractions-augmented temporally contrastive learning: An alternative to the Laplacian in RL. UAI 2022: 641-651 - [i25]Richard S. Sutton, Marlos C. Machado, G. Zacharias Holland, David Szepesvari, Finbarr Timbers, Brian Tanner, Adam White:
Reward-Respecting Subtasks for Model-Based Reinforcement Learning. CoRR abs/2202.03466 (2022) - [i24]Akram Erraqabi, Marlos C. Machado, Mingde Zhao, Sainbayar Sukhbaatar, Alessandro Lazaric, Ludovic Denoyer, Yoshua Bengio:
Temporal Abstractions-Augmented Temporally Contrastive Learning: An Alternative to the Laplacian in RL. CoRR abs/2203.11369 (2022) - [i23]Han Wang, Erfan Miahi, Martha White, Marlos C. Machado, Zaheer Abbas, Raksha Kumaraswamy, Vincent Liu, Adam White:
Investigating the Properties of Neural Network Representations in Reinforcement Learning. CoRR abs/2203.15955 (2022) - [i22]Ruo Yu Tao, Adam White, Marlos C. Machado:
Agent-State Construction with Auxiliary Inputs. CoRR abs/2211.07805 (2022) - 2021
- [c17]Rishabh Agarwal, Marlos C. Machado, Pablo Samuel Castro, Marc G. Bellemare:
Contrastive Behavioral Similarity Embeddings for Generalization in Reinforcement Learning. ICLR 2021 - [c16]Wesley Chung, Valentin Thomas, Marlos C. Machado, Nicolas Le Roux:
Beyond Variance Reduction: Understanding the True Impact of Baselines on Policy Optimization. ICML 2021: 1999-2009 - [i21]Rishabh Agarwal, Marlos C. Machado, Pablo Samuel Castro, Marc G. Bellemare:
Contrastive Behavioral Similarity Embeddings for Generalization in Reinforcement Learning. CoRR abs/2101.05265 (2021) - [i20]Sharan Vaswani, Olivier Bachem, Simone Totaro, Robert Mueller, Matthieu Geist, Marlos C. Machado, Pablo Samuel Castro, Nicolas Le Roux:
A functional mirror ascent view of policy gradient methods with function approximation. CoRR abs/2108.05828 (2021) - [i19]Adrien Ali Taïga, William Fedus, Marlos C. Machado, Aaron C. Courville, Marc G. Bellemare:
On Bonus-Based Exploration Methods in the Arcade Learning Environment. CoRR abs/2109.11052 (2021) - [i18]Marlos C. Machado, André Barreto, Doina Precup:
Temporal Abstraction in Reinforcement Learning with the Successor Representation. CoRR abs/2110.05740 (2021) - 2020
- [j4]Marc G. Bellemare, Salvatore Candido, Pablo Samuel Castro, Jun Gong, Marlos C. Machado, Subhodeep Moitra, Sameera S. Ponda, Ziyu Wang:
Autonomous navigation of stratospheric balloons using reinforcement learning. Nat. 588(7836): 77-82 (2020) - [c15]Marlos C. Machado, Marc G. Bellemare, Michael Bowling:
Count-Based Exploration with the Successor Representation. AAAI 2020: 5125-5133 - [c14]Yuu Jinnai, Jee Won Park, Marlos C. Machado, George Dimitri Konidaris:
Exploration in Reinforcement Learning with Deep Covering Options. ICLR 2020 - [c13]Adrien Ali Taïga, William Fedus, Marlos C. Machado, Aaron C. Courville, Marc G. Bellemare:
On Bonus Based Exploration Methods In The Arcade Learning Environment. ICLR 2020 - [c12]Dibya Ghosh, Marlos C. Machado, Nicolas Le Roux:
An operator view of policy gradient methods. NeurIPS 2020 - [i17]Dibya Ghosh, Marlos C. Machado, Nicolas Le Roux:
An operator view of policy gradient methods. CoRR abs/2006.11266 (2020) - [i16]Wesley Chung, Valentin Thomas, Marlos C. Machado, Nicolas Le Roux:
Beyond variance reduction: Understanding the true impact of baselines on policy optimization. CoRR abs/2008.13773 (2020)
2010 – 2019
- 2019
- [i15]Adrien Ali Taïga, William Fedus, Marlos C. Machado, Aaron C. Courville, Marc G. Bellemare:
Benchmarking Bonus-Based Exploration Methods on the Arcade Learning Environment. CoRR abs/1908.02388 (2019) - 2018
- [j3]Marlos C. Machado, Marc G. Bellemare, Erik Talvitie, Joel Veness, Matthew J. Hausknecht, Michael Bowling:
Revisiting the Arcade Learning Environment: Evaluation Protocols and Open Problems for General Agents. J. Artif. Intell. Res. 61: 523-562 (2018) - [c11]Marlos C. Machado, Clemens Rosenbaum, Xiaoxiao Guo, Miao Liu, Gerald Tesauro, Murray Campbell:
Eigenoption Discovery through the Deep Successor Representation. ICLR (Poster) 2018 - [c10]Marlos C. Machado, Marc G. Bellemare, Erik Talvitie, Joel Veness, Matthew J. Hausknecht, Michael Bowling:
Revisiting the Arcade Learning Environment: Evaluation Protocols and Open Problems for General Agents (Extended Abstract). IJCAI 2018: 5573-5577 - [c9]Craig Sherstan, Marlos C. Machado, Patrick M. Pilarski:
Accelerating Learning in Constructive Predictive Frameworks with the Successor Representation. IROS 2018: 2997-3003 - [i14]Craig Sherstan, Marlos C. Machado, Patrick M. Pilarski:
Accelerating Learning in Constructive Predictive Frameworks with the Successor Representation. CoRR abs/1803.09001 (2018) - [i13]Marlos C. Machado, Marc G. Bellemare, Michael Bowling:
Count-Based Exploration with the Successor Representation. CoRR abs/1807.11622 (2018) - [i12]Jesse Farebrother, Marlos C. Machado, Michael Bowling:
Generalization and Regularization in DQN. CoRR abs/1810.00123 (2018) - 2017
- [c8]Marlos C. Machado, Marc G. Bellemare, Michael H. Bowling:
A Laplacian Framework for Option Discovery in Reinforcement Learning. ICML 2017: 2295-2304 - [i11]Marlos C. Machado, Marc G. Bellemare, Michael H. Bowling:
A Laplacian Framework for Option Discovery in Reinforcement Learning. CoRR abs/1703.00956 (2017) - [i10]Marlos C. Machado, Marc G. Bellemare, Erik Talvitie, Joel Veness, Matthew J. Hausknecht, Michael Bowling:
Revisiting the Arcade Learning Environment: Evaluation Protocols and Open Problems for General Agents. CoRR abs/1709.06009 (2017) - [i9]Marlos C. Machado, Clemens Rosenbaum, Xiaoxiao Guo, Miao Liu, Gerald Tesauro, Murray Campbell:
Eigenoption Discovery through the Deep Successor Representation. CoRR abs/1710.11089 (2017) - [i8]Miao Liu, Marlos C. Machado, Gerald Tesauro, Murray Campbell:
The Eigenoption-Critic Framework. CoRR abs/1712.04065 (2017) - 2016
- [j2]Harm van Seijen, Ashique Rupam Mahmood, Patrick M. Pilarski, Marlos C. Machado, Richard S. Sutton:
True Online Temporal-Difference Learning. J. Mach. Learn. Res. 17: 145:1-145:40 (2016) - [c7]Craig Sherstan, Adam White, Marlos C. Machado, Patrick M. Pilarski:
Introspective Agents: Confidence Measures for General Value Functions. AGI 2016: 258-261 - [c6]Yitao Liang, Marlos C. Machado, Erik Talvitie, Michael H. Bowling:
State of the Art Control of Atari Games Using Shallow Reinforcement Learning. AAMAS 2016: 485-493 - [i7]Marlos C. Machado, Michael H. Bowling:
Learning Purposeful Behaviour in the Absence of Rewards. CoRR abs/1605.07700 (2016) - [i6]Craig Sherstan, Adam White, Marlos C. Machado, Patrick M. Pilarski:
Introspective Agents: Confidence Measures for General Value Functions. CoRR abs/1606.05593 (2016) - 2015
- [c5]Marlos C. Machado, Sriram Srinivasan, Michael H. Bowling:
Domain-Independent Optimistic Initialization for Reinforcement Learning. AAAI Workshop: Learning for General Competency in Video Games 2015 - [e1]Michael Bowling, Marc G. Bellemare, Erik Talvitie, Joel Veness, Marlos C. Machado:
Learning for General Competency in Video Games, Papers from the 2015 AAAI Workshop, Austin, Texas, USA, January 26, 2015. AAAI Technical Report WS-15-10, AAAI Press 2015, ISBN 978-1-57735-721-6 [contents] - [i5]Stefano V. Albrecht, J. Christopher Beck, David L. Buckeridge, Adi Botea, Cornelia Caragea, Chi-Hung Chi, Theodoros Damoulas, Bistra Dilkina, Eric Eaton, Pooyan Fazli, Sam Ganzfried, Marius Lindauer, Marlos C. Machado, Yuri Malitsky, Gary Marcus, Sebastiaan A. Meijer, Francesca Rossi, Arash Shaban-Nejad, Sylvie Thiébaux, Manuela M. Veloso, Toby Walsh, Can Wang, Jie Zhang, Yu Zheng:
Reports from the 2015 AAAI Workshop Program. AI Mag. 36(2): 90-101 (2015) - [i4]Yitao Liang, Marlos C. Machado, Erik Talvitie, Michael H. Bowling:
State of the Art Control of Atari Games Using Shallow Reinforcement Learning. CoRR abs/1512.01563 (2015) - [i3]Harm van Seijen, Ashique Rupam Mahmood, Patrick M. Pilarski, Marlos C. Machado, Richard S. Sutton:
True Online Temporal-Difference Learning. CoRR abs/1512.04087 (2015) - 2014
- [j1]Renato Luiz de Freitas Cunha, Marlos C. Machado, Luiz Chaimowicz:
RTSMate: Towards an Advice System for RTS Games. Comput. Entertain. 12(1): 1:1-1:20 (2014) - [i2]Marlos C. Machado, Sriram Srinivasan, Michael Bowling:
Domain-Independent Optimistic Initialization for Reinforcement Learning. CoRR abs/1410.4604 (2014) - 2013
- [i1]Marlos C. Machado:
A Methodology for Player Modeling based on Machine Learning. CoRR abs/1312.3903 (2013) - 2012
- [c4]Marlos C. Machado, Gisele L. Pappa, Luiz Chaimowicz:
A binary classification approach for automatic preference modeling of virtual agents in Civilization IV. CIG 2012: 155-162 - 2011
- [c3]Marlos C. Machado, Eduardo P. C. Fantini, Luiz Chaimowicz:
Player modeling: Towards a common taxonomy. CGAMES 2011: 50-57 - [c2]Marlos C. Machado, Bruno S. L. Rocha, Luiz Chaimowicz:
Agents Behavior and Preferences Characterization in Civilization IV. SBGames 2011: 43-52 - [c1]Marlos C. Machado, Luiz Chaimowicz:
Combining Metaheuristics and CSP Algorithms to Solve Sudoku. SBGames 2011: 124-131
Coauthor Index
Michael H. Bowling
aka: Michael Bowling
aka: Michael Bowling
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last updated on 2026-08-07 22:32 CEST by the dblp team
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