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04/27/2023
Discovering Object-Centric Generalized Value Functions From Pixels
Deep Reinforcement Learning has shown significant progress in extracting...
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12/30/2022
POMRL: No-Regret Learning-to-Plan with Increasing Horizons
We study the problem of planning under model uncertainty in an online me...
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01/24/2022
The Paradox of Choice: Using Attention in Hierarchical Reinforcement Learning
Decision-making AI agents are often faced with two important challenges:...
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08/06/2021
Temporally Abstract Partial Models
Humans and animals have the ability to reason and make predictions about...
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08/02/2021
Sequoia: A Software Framework to Unify Continual Learning Research
The field of Continual Learning (CL) seeks to develop algorithms that ac...
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12/25/2020
Towards Continual Reinforcement Learning: A Review and Perspectives
In this article, we aim to provide a literature review of different form...
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07/14/2020
Multi-Task Reinforcement Learning as a Hidden-Parameter Block MDP
Multi-task reinforcement learning is a rich paradigm where information f...
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06/26/2020
What can I do here? A Theory of Affordances in Reinforcement Learning
Reinforcement learning algorithms usually assume that all actions are al...
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01/01/2020
Options of Interest: Temporal Abstraction with Interest Functions
Temporal abstraction refers to the ability of an agent to use behaviours...
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11/26/2018
Environments for Lifelong Reinforcement Learning
To achieve general artificial intelligence, reinforcement learning (RL) ...
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07/25/2018
Attend Before you Act: Leveraging human visual attention for continual learning
When humans perform a task, such as playing a game, they selectively pay...
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07/21/2018
