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Oral
Been There, Done That: Meta-Learning with Episodic Recall
Samuel Ritter · Jane Wang · Zeb Kurth-Nelson · Siddhant Jayakumar · Charles Blundell · Razvan Pascanu · Matthew Botvinick

Wed Jul 11 08:40 AM -- 08:50 AM (PDT) @ A3
in Reinforcement Learning 7 »

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 – meta-learning 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.

Author Information

Sam Ritter (DeepMind)
Jane Wang (DeepMind)
Zeb Kurth-Nelson (DeepMind)
Siddhant Jayakumar (DeepMind)
Charles Blundell (DeepMind)
Razvan Pascanu (DeepMind)
Matthew Botvinick (DeepMind)

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