Player modeling: Towards a common taxonomy. - Marlos C. Machado; Eduardo P. C. Fantini; Luiz Chaimowicz (CGAMES, 2011)

Combining Metaheuristics and CSP Algorithms to Solve Sudoku. - Marlos C. Machado; Luiz Chaimowicz (SBGames, 2011)

Agents Behavior and Preferences Characterization in Civilization IV. - Marlos C. Machado; Bruno S. L. Rocha; Luiz Chaimowicz (SBGames, 2011)

A binary classification approach for automatic preference modeling of virtual agents in Civilization IV. - Marlos C. Machado; Gisele L. Pappa; Luiz Chaimowicz (CIG, 2012)

A Methodology for Player Modeling based on Machine Learning. - Marlos C. Machado (CoRR, 2013)

RTSMate: Towards an Advice System for RTS Games. - Renato Luiz de Freitas Cunha; Marlos C. Machado; Luiz Chaimowicz (Comput. Entertain., 2014)

Domain-Independent Optimistic Initialization for Reinforcement Learning. - Marlos C. Machado; Sriram Srinivasan 005; Michael H. Bowling (AAAI Workshop: Learning for General Competency in Video Games, 2015)

Reports from the 2015 AAAI Workshop Program. - 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; et al. (AI Mag., 2015)

Introspective Agents: Confidence Measures for General Value Functions. - Craig Sherstan; Adam White 001; Marlos C. Machado; Patrick M. Pilarski (AGI, 2016)

State of the Art Control of Atari Games Using Shallow Reinforcement Learning. - Yitao Liang; Marlos C. Machado; Erik Talvitie; Michael H. Bowling (AAMAS, 2016)

Learning Purposeful Behaviour in the Absence of Rewards. - Marlos C. Machado; Michael H. Bowling (CoRR, 2016)

True Online Temporal-Difference Learning. - Harm van Seijen; Ashique Rupam Mahmood; Patrick M. Pilarski; Marlos C. Machado; Richard S. Sutton (J. Mach. Learn. Res., 2016)

A Laplacian Framework for Option Discovery in Reinforcement Learning. - Marlos C. Machado; Marc G. Bellemare; Michael H. Bowling (ICML, 2017)

The Eigenoption-Critic Framework. - Miao Liu 001; Marlos C. Machado; Gerald Tesauro; Murray Campbell (CoRR, 2017)

Eigenoption Discovery through the Deep Successor Representation. - Marlos C. Machado; Clemens Rosenbaum; Xiaoxiao Guo; Miao Liu 001; Gerald Tesauro; Murray Campbell (ICLR (Poster), 2018)

Revisiting the Arcade Learning Environment: Evaluation Protocols and Open Problems for General Agents (Extended Abstract). - Marlos C. Machado; Marc G. Bellemare; Erik Talvitie; Joel Veness; Matthew J. Hausknecht; Michael Bowling (IJCAI, 2018)

Accelerating Learning in Constructive Predictive Frameworks with the Successor Representation. - Craig Sherstan; Marlos C. Machado; Patrick M. Pilarski (IROS, 2018)

Generalization and Regularization in DQN. - Jesse Farebrother; Marlos C. Machado; Michael Bowling (CoRR, 2018)

Revisiting the Arcade Learning Environment: Evaluation Protocols and Open Problems for General Agents. - Marlos C. Machado; Marc G. Bellemare; Erik Talvitie; Joel Veness; Matthew J. Hausknecht; Michael Bowling (J. Artif. Intell. Res., 2018)

Benchmarking Bonus-Based Exploration Methods on the Arcade Learning Environment. - Adrien Ali Taïga; William Fedus; Marlos C. Machado; Aaron C. Courville; Marc G. Bellemare (CoRR, 2019)

Count-Based Exploration with the Successor Representation. - Marlos C. Machado; Marc G. Bellemare; Michael Bowling (AAAI, 2020)

Exploration in Reinforcement Learning with Deep Covering Options. - Yuu Jinnai; Jee Won Park; Marlos C. Machado; George Dimitri Konidaris (ICLR, 2020)

On Bonus Based Exploration Methods In The Arcade Learning Environment. - Adrien Ali Taïga; William Fedus; Marlos C. Machado; Aaron C. Courville; Marc G. Bellemare (ICLR, 2020)

An operator view of policy gradient methods. - Dibya Ghosh; Marlos C. Machado; Nicolas Le Roux (NeurIPS, 2020)

Autonomous navigation of stratospheric balloons using reinforcement learning. - Marc G. Bellemare; Salvatore Candido; Pablo Samuel Castro; Jun Gong; Marlos C. Machado; Subhodeep Moitra; Sameera S. Ponda; Ziyu Wang (Nat., 2020)

Contrastive Behavioral Similarity Embeddings for Generalization in Reinforcement Learning. - Rishabh Agarwal; Marlos C. Machado; Pablo Samuel Castro; Marc G. Bellemare (ICLR, 2021)

Beyond Variance Reduction: Understanding the True Impact of Baselines on Policy Optimization. - Wesley Chung; Valentin Thomas; Marlos C. Machado; Nicolas Le Roux (ICML, 2021)

A functional mirror ascent view of policy gradient methods with function approximation. - Sharan Vaswani; Olivier Bachem; Simone Totaro; Robert Mueller; Matthieu Geist; Marlos C. Machado; Pablo Samuel Castro; Nicolas Le Roux (CoRR, 2021)

Temporal Abstraction in Reinforcement Learning with the Successor Representation. - Marlos C. Machado; André Barreto 001; Doina Precup (CoRR, 2021)

A general class of surrogate functions for stable and efficient reinforcement learning. - Sharan Vaswani; Olivier Bachem; Simone Totaro; Robert Müller; Shivam Garg; Matthieu Geist; Marlos C. Machado; Pablo Samuel Castro; Nicolas Le Roux (AISTATS, 2022)

Temporal abstractions-augmented temporally contrastive learning: An alternative to the Laplacian in RL. - Akram Erraqabi; Marlos C. Machado; Mingde Zhao 001; Sainbayar Sukhbaatar; Alessandro Lazaric; Ludovic Denoyer; Yoshua Bengio (UAI, 2022)

Loss of Plasticity in Continual Deep Reinforcement Learning. - Zaheer Abbas; Rosie Zhao; Joseph Modayil; Adam White 001; Marlos C. Machado (CoLLAs, 2023)

Trajectory-Aware Eligibility Traces for Off-Policy Reinforcement Learning. - Brett Daley; Martha White; Christopher Amato; Marlos C. Machado (ICML, 2023)

Deep Laplacian-based Options for Temporally-Extended Exploration. - Martin Klissarov; Marlos C. Machado (ICML, 2023)

Recurrent Linear Transformers. - Subhojeet Pramanik; Esraa Elelimy; Marlos C. Machado; Adam White 001 (CoRR, 2023)

Curvature Explains Loss of Plasticity. - Alex Lewandowski; Haruto Tanaka; Dale Schuurmans; Marlos C. Machado (CoRR, 2023)

Temporal Abstraction in Reinforcement Learning with the Successor Representation. - Marlos C. Machado; André Barreto 001; Doina Precup; Michael Bowling (J. Mach. Learn. Res., 2023)

Agent-State Construction with Auxiliary Inputs. - Ruo Yu Tao; Adam White 001; Marlos C. Machado (Trans. Mach. Learn. Res., 2023)

Reward-respecting subtasks for model-based reinforcement learning. - Richard S. Sutton; Marlos C. Machado; G. Zacharias Holland; David Szepesvari; Finbarr Timbers; Brian Tanner; Adam White 001 (Artif. Intell., 2023)

Reward-Respecting Subtasks for Model-Based Reinforcement Learning (Abstract Reprint). - Richard S. Sutton; Marlos C. Machado; G. Zacharias Holland; David Szepesvari; Finbarr Timbers; Brian Tanner; Adam White 001 (AAAI, 2024)

Proper Laplacian Representation Learning. - Diego Gomez; Michael Bowling; Marlos C. Machado (ICLR, 2024)

Averaging n-step Returns Reduces Variance in Reinforcement Learning. - Brett Daley; Martha White; Marlos C. Machado (ICML, 2024)

Demystifying the Recency Heuristic in Temporal-Difference Learning. - Brett Daley; Marlos C. Machado; Martha White (RLJ, 2024)

Harnessing Discrete Representations for Continual Reinforcement Learning. - Edan Meyer; Adam White 001; Marlos C. Machado (RLJ, 2024)

Investigating the properties of neural network representations in reinforcement learning. - Han Wang; Erfan Miahi; Martha White; Marlos C. Machado; Zaheer Abbas; Raksha Kumaraswamy; Vincent Liu; Adam White 001 (Artif. Intell., 2024)

Compound Returns Reduce Variance in Reinforcement Learning. - Brett Daley; Martha White; Marlos C. Machado (CoRR, 2024)

Learning Continually by Spectral Regularization. - Alex Lewandowski; Saurabh Kumar 004; Dale Schuurmans; András György 001; Marlos C. Machado (CoRR, 2024)

AGaLiTe: Approximate Gated Linear Transformers for Online Reinforcement Learning. - Subhojeet Pramanik; Esraa Elelimy; Marlos C. Machado; Adam White 001 (Trans. Mach. Learn. Res., 2024)

GVFs in the real world: making predictions online for water treatment. - Muhammad Kamran Janjua; Haseeb Shah; Martha White; Erfan Miahi; Marlos C. Machado; Adam White 001 (Mach. Learn., 2024)

Representation-driven Option Discovery in Reinforcement Learning. - Marlos C. Machado (AAAI, 2025)

MaestroMotif: Skill Design from Artificial Intelligence Feedback. - Martin Klissarov; Mikael Henaff; Roberta Raileanu; Shagun Sodhani; Pascal Vincent; Amy Zhang 001; Pierre-Luc Bacon; Doina Precup; Marlos C. Machado; Pierluca D'Oro (ICLR, 2025)

Learning Continually by Spectral Regularization. - Alex Lewandowski; Michal Bortkiewicz; Saurabh Kumar 004; András György 001; Dale Schuurmans; Mateusz Ostaszewski; Marlos C. Machado (ICLR, 2025)

Plastic Learning with Deep Fourier Features. - Alex Lewandowski; Dale Schuurmans; Marlos C. Machado (ICLR, 2025)

Reward-Aware Proto-Representations in Reinforcement Learning. - Hon Tik Tse; Siddarth Chandrasekar; Marlos C. Machado (CoRR, 2025)

The Cell Must Go On: Agar.io for Continual Reinforcement Learning. - Mohamed A. Mohamed; Kateryna Nekhomiazh; Vedant Vyas; Marcos M. Jose; Andrew Patterson; Marlos C. Machado (CoRR, 2025)

Discovering Temporal Structure: An Overview of Hierarchical Reinforcement Learning. - Martin Klissarov; Akhil Bagaria; Ziyan Luo; George Dimitri Konidaris; Doina Precup; Marlos C. Machado (CoRR, 2025)

Double Q-learning for Value-based Deep Reinforcement Learning, Revisited. - Prabhat Nagarajan; Martha White; Marlos C. Machado (CoRR, 2025)

Deep Reinforcement Learning with Gradient Eligibility Traces. - Esraa Elelimy; Brett Daley; Andrew Patterson; Marlos C. Machado; Adam White 001; Martha White (CoRR, 2025)

A Study of Value-Aware Eigenoptions. - Harshil Kotamreddy; Marlos C. Machado (CoRR, 2025)

An Analysis of Action-Value Temporal-Difference Methods That Learn State Values. - Brett Daley; Prabhat Nagarajan; Martha White; Marlos C. Machado (CoRR, 2025)