# Suggested further readings 

## Tutorial 1: The problem of changing data distributions

- [Environment and Distribution Shift](https://d2l.ai/chapter_linear-classification/environment-and-distribution-shift.html)

## Tutorial 2: Continual learning

- [Continual Lifelong Learning with Neural Networks: A Review](https://arxiv.org/pdf/1802.07569)
- [ContinualAI](https://www.continualai.org/)
- [A Comprehensive Survey of Continual Learning: Theory, Method and Application](https://arxiv.org/pdf/2302.00487)
- [Brain-inspired replay for continual learning with artificial neural networks](https://www.nature.com/articles/s41467-020-17866-2)

## Tutorial 3: Meta-learning

- [Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks](https://arxiv.org/abs/1703.03400)
- [An Interactive Introduction to Model-Agnostic Meta-Learning](https://interactive-maml.github.io/maml.html)

## Tutorial 4: Biological meta reinforcement learning  

- [Meta-Learning by the Baldwin Effect](https://arxiv.org/pdf/1806.07917)
- [Prefrontal cortex as a meta-reinforcement learning system](https://www.nature.com/articles/s41593-018-0147-8)
- [Reinforcement Learning, Fast and Slow](https://www.cell.com/action/showPdf?pii=S1364-6613%2819%2930061-0)

## Tutorial 5: Replay

- [Experience Replay for Continual Learning](https://arxiv.org/abs/1811.11682)