360 citations · 2.7k across the 150 of their papers we have counts for
5 papers · 2 filters
Environments for Lifelong Reinforcement Learning
Khimya Khetarpal, Shagun Sodhani, Sarath Chandar +1
To achieve general artificial intelligence, reinforcement learning (RL) agents should learn not only to optimize returns for one specific task but also to constantly build more com…
The Barbados 2018 List of Open Issues in Continual Learning
Tom Schaul, Hado van Hasselt, Joseph Modayil +7
We want to make progress toward artificial general intelligence, namely general-purpose agents that autonomously learn how to competently act in complex environments. The purpose o…
Attend Before you Act: Leveraging human visual attention for continual learning
Khimya Khetarpal, Doina Precup
When humans perform a task, such as playing a game, they selectively pay attention to certain parts of the visual input, gathering relevant information and sequentially combining i…
Safe Option-Critic: Learning Safety in the Option-Critic Architecture
Arushi Jain, Khimya Khetarpal, Doina Precup
Designing hierarchical reinforcement learning algorithms that exhibit safe behaviour is not only vital for practical applications but also, facilitates a better understanding of an…
Learning Robust Options
Daniel J. Mankowitz, Timothy A. Mann, Pierre-Luc Bacon +2
Robust reinforcement learning aims to produce policies that have strong guarantees even in the face of environments/transition models whose parameters have strong uncertainty. Exis…