# The Barbados 2018 List of Open Issues in Continual Learning

arXiv: https://arxiv.org/abs/1811.07004 · HTML: https://mlsanity.com/paper/1811.07004/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Nov 2018.

## Built on by (most cited first, 7 of 7)

- [A Definition of Continual Reinforcement Learning](https://mlsanity.com/paper/2307.11046/) (2023)
- [Three Dogmas of Reinforcement Learning](https://mlsanity.com/paper/2407.10583/) (2024)
- [Exploring through Random Curiosity with General Value Functions](https://mlsanity.com/paper/2211.10282/) (2022)
- [A Look at Value-Based Decision-Time vs. Background Planning Methods Across Different Settings](https://mlsanity.com/paper/2206.08442/) (2022)
- [Probing Transfer in Deep Reinforcement Learning without Task Engineering](https://mlsanity.com/paper/2210.12448/) (2022)
- [Minimal Value-Equivalent Partial Models for Scalable and Robust Planning in Lifelong Reinforcement Learning](https://mlsanity.com/paper/2301.10119/) (2023)
- [When Remembering and Planning are Worth it: Navigating under Change](https://mlsanity.com/paper/2602.15274/) (2026)

## Abstract (arXiv)

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.