PT  - JOURNAL ARTICLE
AU  - S Ritter
AU  - JX Wang
AU  - Z Kurth-Nelson
AU  - M Botvinick
TI  - Episodic Control as Meta-Reinforcement Learning
AID  - 10.1101/360537
DP  - 2018 Jan 01
TA  - bioRxiv
PG  - 360537
4099  - http://biorxiv.org/content/early/2018/07/03/360537.short
4100  - http://biorxiv.org/content/early/2018/07/03/360537.full
AB  - Recent research has placed episodic reinforcement learning (RL) alongside model-free and model-based RL on the list of processes centrally involved in human reward-based learning. In the present work, we extend the unified account of model-free and model-based RL developed by Wang et al. (2018) to further integrate episodic learning. In this account, a generic model-free “meta-learner” learns to deploy and coordinate among all of these learning algorithms. The meta-learner learns through brief encounters with many novel tasks, so that it learns to learn about new tasks. We show that when equipped with an episodic memory system inspired by theories of reinstatement and gating, the meta-learner learns to use the episodic and model-based learning algorithms observed in humans in a task designed to dissociate among the influences of various learning strategies. We discuss implications and predictions of the model.