David Abel

CoRR, 2022

On the Expressivity of Markov Reward (Extended Abstract).

[BibT_eX]

[DOI]

Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, 2022

Meta-Gradients in Non-Stationary Environments.

[BibT_eX]

[DOI]

Proceedings of the Conference on Lifelong Learning Agents, 2022

2021

Bad-Policy Density: A Measure of Reinforcement Learning Hardness.

[BibT_eX]

[DOI]

CoRR, 2021

Control of mental representations in human planning.

[BibT_eX]

[DOI]

CoRR, 2021

On the Expressivity of Markov Reward.

[BibT_eX]

[DOI]

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Revisiting Peng's Q(λ) for Modern Reinforcement Learning.

[BibT_eX]

[DOI]

Proceedings of the 38th International Conference on Machine Learning, 2021

Lipschitz Lifelong Reinforcement Learning.

[BibT_eX]

[DOI]

Proceedings of the Thirty-Fifth AAAI Conference on Artificial Intelligence, 2021

2020

A Theory of Abstraction in Reinforcement Learning.

[BibT_eX]

[DOI]

PhD thesis, 2020

The Efficiency of Human Cognition Reflects Planned Information Processing.

[BibT_eX]

[DOI]

CoRR, 2020

Learning State Abstractions for Transfer in Continuous Control.

[BibT_eX]

[DOI]

Kavosh Asadi

Michael Littman

CoRR, 2020

What can I do here? A Theory of Affordances in Reinforcement Learning.

[BibT_eX]

[DOI]

Proceedings of the 37th International Conference on Machine Learning, 2020

Value Preserving State-Action Abstractions.

[BibT_eX]

[DOI]

Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics, 2020

People Do Not Just Plan, They Plan to Plan.

[BibT_eX]

[DOI]

Proceedings of the Thirty-Fourth AAAI Conference on Artificial Intelligence, 2020

2019

The Expected-Length Model of Options.

[BibT_eX]

[DOI]

Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence, 2019

Discovering Options for Exploration by Minimizing Cover Time.

[BibT_eX]

[DOI]

Jee Won Park

Proceedings of the 36th International Conference on Machine Learning, 2019

Finding Options that Minimize Planning Time.

[BibT_eX]

[DOI]

David Ellis Hershkowitz

Proceedings of the 36th International Conference on Machine Learning, 2019

simple_rl: Reproducible Reinforcement Learning in Python.

[BibT_eX]

[DOI]

Proceedings of the Reproducibility in Machine Learning, 2019

State Abstraction as Compression in Apprenticeship Learning.

[BibT_eX]

[DOI]

Proceedings of the Thirty-Third AAAI Conference on Artificial Intelligence, 2019

A Theory of State Abstraction for Reinforcement Learning.

[BibT_eX]

[DOI]

Proceedings of the Thirty-Third AAAI Conference on Artificial Intelligence, 2019

2018

Mitigating Planner Overfitting in Model-Based Reinforcement Learning.

[BibT_eX]

[DOI]

CoRR, 2018

Finding Options that Minimize Planning Time.

[BibT_eX]

[DOI]

CoRR, 2018

Policy and Value Transfer in Lifelong Reinforcement Learning.

[BibT_eX]

[DOI]

Sophie Yue Guo

Proceedings of the 35th International Conference on Machine Learning, 2018

State Abstractions for Lifelong Reinforcement Learning.

[BibT_eX]

[DOI]

Proceedings of the 35th International Conference on Machine Learning, 2018

Bandit-Based Solar Panel Control.

[BibT_eX]

[DOI]

Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence, 2018

2017

Latent Attention Networks.

[BibT_eX]

[DOI]

CoRR, 2017

Agent-Agnostic Human-in-the-Loop Reinforcement Learning.

[BibT_eX]

[DOI]

CoRR, 2017

2016

Exploratory Gradient Boosting for Reinforcement Learning in Complex Domains.

[BibT_eX]

[DOI]

CoRR, 2016

Near Optimal Behavior via Approximate State Abstraction.

[BibT_eX]

[DOI]

D. Ellis Hershkowitz

Proceedings of the 33nd International Conference on Machine Learning, 2016

Reinforcement Learning as a Framework for Ethical Decision Making.

[BibT_eX]

[DOI]

James MacGlashan