The Barbados 2018 List of Open Issues in Continual Learning
By Tom Schaul, Hado van Hasselt, Joseph Modayil, Martha White, Adam White, Pierre-Luc Bacon, Jean Harb, Shibl Mourad, Marc G. Bellemare, Doina Precup. 2018
12 citations recorded by the source corpus.
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 of this report is to sketch a research outline, share some of the most important open issues we are facing, and stimulate further discussion in the community. The content is based on some of our discussions during a week-long workshop held in Barbados in February 2018.
Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Sergey Levine
- Yoshua Bengio
- Matthew Riemer
- Doina Precup
- Razvan Pascanu
- Irina Rish
- Pieter Abbeel
- Joelle Pineau
In Computer Vision and Pattern Recognition
- Matthew Riemer
- Razvan Pascanu
- Irina Rish
- Doina Precup
- Tom Schaul
- Diana Borsa
- Joseph Modayil
- Khimya Khetarpal
Citations in the corpus
- Towards Continual Reinforcement Learning: A Review and Perspectives (2020, background): “We refer the reader to (Schaul et al., 2018) for more discussion on open problems in continual RL. Experiment Design and Evaluation.”
- Ray Interference: a Source of Plateaus in Deep Reinforcement Learning (2019, background): “Head-on approaches to alleviate ray interference could draw from the growing body of continual learning techniques [22, 29, 30, 36, 40].”