Been There Done That Metalearning With Episodic Recall

# Been There, Done That: Meta-Learning with Episodic Recall

**arXiv ID:** 1805.09692
**Authors:** Samuel Ritter, Jane X. Wang, Zeb Kurth-Nelson, Siddhant M. Jayakumar, Charles Blundell, Razvan Pascanu, Matthew Botvinick
**Published:** 2018-05-24T14:15:27Z
**Abstract:**
Meta-learning agents excel at rapidly learning new tasks from open-ended task distributions; yet, they forget what they learn about each task as soon as the next begins. When tasks reoccur - as they do in natural environments - metalearning agents must explore again instead of immediately exploiting previously discovered solutions. We propose a formalism for generating open-ended yet repetitious environments, then develop a meta-learning architecture for solving these environments. This architecture melds the standard LSTM working memory with a differentiable neural episodic memory. We explore the capabilities of agents with this episodic LSTM in five meta-learning environments with reoccurring tasks, ranging from bandits to navigation and stochastic sequential decision problems.

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## References

- [arXiv:1805.09692](http://arxiv.org/abs/1805.09692v2)