Marlos C. Machado

Orcid: 0000-0002-0117-6134

Affiliations:
  • Department of Computing Science, University of Alberta, Canada
  • Federal University of Minas Gerais, Belo Horizonte, Brazil (former)


According to our database1, Marlos C. Machado authored at least 45 papers between 2011 and 2024.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

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Bibliography

2024
Compound Returns Reduce Variance in Reinforcement Learning.
CoRR, 2024

Reward-Respecting Subtasks for Model-Based Reinforcement Learning (Abstract Reprint).
Proceedings of the Thirty-Eighth AAAI Conference on Artificial Intelligence, 2024

2023
Reward-respecting subtasks for model-based reinforcement learning.
Artif. Intell., November, 2023

Temporal Abstraction in Reinforcement Learning with the Successor Representation.
J. Mach. Learn. Res., 2023

GVFs in the Real World: Making Predictions Online for Water Treatment.
CoRR, 2023

Harnessing Discrete Representations For Continual Reinforcement Learning.
CoRR, 2023

Curvature Explains Loss of Plasticity.
CoRR, 2023

Recurrent Linear Transformers.
CoRR, 2023

Proper Laplacian Representation Learning.
CoRR, 2023

Deep Laplacian-based Options for Temporally-Extended Exploration.
Proceedings of the International Conference on Machine Learning, 2023

Trajectory-Aware Eligibility Traces for Off-Policy Reinforcement Learning.
Proceedings of the International Conference on Machine Learning, 2023

Loss of Plasticity in Continual Deep Reinforcement Learning.
Proceedings of the Conference on Lifelong Learning Agents, 2023

2022
Agent-State Construction with Auxiliary Inputs.
CoRR, 2022

Investigating the Properties of Neural Network Representations in Reinforcement Learning.
CoRR, 2022

Temporal abstractions-augmented temporally contrastive learning: An alternative to the Laplacian in RL.
Proceedings of the Uncertainty in Artificial Intelligence, 2022

A general class of surrogate functions for stable and efficient reinforcement learning.
Proceedings of the International Conference on Artificial Intelligence and Statistics, 2022

2021
Temporal Abstraction in Reinforcement Learning with the Successor Representation.
CoRR, 2021

A functional mirror ascent view of policy gradient methods with function approximation.
CoRR, 2021

Beyond Variance Reduction: Understanding the True Impact of Baselines on Policy Optimization.
Proceedings of the 38th International Conference on Machine Learning, 2021

Contrastive Behavioral Similarity Embeddings for Generalization in Reinforcement Learning.
Proceedings of the 9th International Conference on Learning Representations, 2021

2020
Autonomous navigation of stratospheric balloons using reinforcement learning.
Nat., 2020

An operator view of policy gradient methods.
Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

On Bonus Based Exploration Methods In The Arcade Learning Environment.
Proceedings of the 8th International Conference on Learning Representations, 2020

Exploration in Reinforcement Learning with Deep Covering Options.
Proceedings of the 8th International Conference on Learning Representations, 2020

Count-Based Exploration with the Successor Representation.
Proceedings of the Thirty-Fourth AAAI Conference on Artificial Intelligence, 2020

2019
Benchmarking Bonus-Based Exploration Methods on the Arcade Learning Environment.
CoRR, 2019

2018
Revisiting the Arcade Learning Environment: Evaluation Protocols and Open Problems for General Agents.
J. Artif. Intell. Res., 2018

Generalization and Regularization in DQN.
CoRR, 2018

Accelerating Learning in Constructive Predictive Frameworks with the Successor Representation.
Proceedings of the 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems, 2018

Revisiting the Arcade Learning Environment: Evaluation Protocols and Open Problems for General Agents (Extended Abstract).
Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence, 2018

Eigenoption Discovery through the Deep Successor Representation.
Proceedings of the 6th International Conference on Learning Representations, 2018

2017
The Eigenoption-Critic Framework.
CoRR, 2017

A Laplacian Framework for Option Discovery in Reinforcement Learning.
Proceedings of the 34th International Conference on Machine Learning, 2017

2016
True Online Temporal-Difference Learning.
J. Mach. Learn. Res., 2016

Learning Purposeful Behaviour in the Absence of Rewards.
CoRR, 2016

State of the Art Control of Atari Games Using Shallow Reinforcement Learning.
Proceedings of the 2016 International Conference on Autonomous Agents & Multiagent Systems, 2016

Introspective Agents: Confidence Measures for General Value Functions.
Proceedings of the Artificial General Intelligence - 9th International Conference, 2016

2015
Reports from the 2015 AAAI Workshop Program.
AI Mag., 2015

Domain-Independent Optimistic Initialization for Reinforcement Learning.
Proceedings of the Learning for General Competency in Video Games, 2015

2014
RTSMate: Towards an Advice System for RTS Games.
Comput. Entertain., 2014

2013
A Methodology for Player Modeling based on Machine Learning.
CoRR, 2013

2012
A binary classification approach for automatic preference modeling of virtual agents in Civilization IV.
Proceedings of the 2012 IEEE Conference on Computational Intelligence and Games, 2012

2011
Agents Behavior and Preferences Characterization in Civilization IV.
Proceedings of the 2011 Brazilian Symposium on Games and Digital Entertainment, 2011

Combining Metaheuristics and CSP Algorithms to Solve Sudoku.
Proceedings of the 2011 Brazilian Symposium on Games and Digital Entertainment, 2011

Player modeling: Towards a common taxonomy.
Proceedings of the 16th International Conference on Computer Games, 2011


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