Learning to use episodic memory Action editor: Andrew Howes Nicholas A. Gorski*, John E. Laird Computer Science & Engineering, University of Michigan, 2260 Hayward St., Ann Arbor, MI 48109-2121, USA Received 22 December 2009; accepted 29 June 2010 Available online 8 August 2010 Abstract This paper brings together work in modeling episodic memory and reinforcement learning (RL). In particular, inspired by curious behaviour in animals, observing something novel could be rewarded with a bonus. We demonstrate that is possible to learn to use episodic memory retrievals while … Print 2019 Jul. that leverages an episodic-like memory to predict upcoming events, which 'speaks’ to a reinforcement-learning module that selects actions based on the predictor module's current state. We propose Neural Episodic Control: a deep rein-forcement learning agent that is able to rapidly assimilate new experiences and act upon them. As opposed to other RL systems, EC enables rapidly learning a policy from sparse amounts of experience. The Google Brain team with DeepMind and ETH Zurich have introduced an episodic memory-based curiosity model which allows Reinforcement Learning (RL) agents to explore environments in an intelligent way. Crossref; PubMed; Scopus (47) Google Scholar, 42. reinforcement learning models. Episodic memory is a psychology term which refers to the ability to recall specific events from the past. Despite the success, deep RL algorithms are known to be sample inefcient, often requiring many rounds of interaction with the environments to obtain satis-factory performance. Instead of using the Euclidean distance to measure closeness of states in episodic memory, Savinov, et al. Reinforcement Learning and Episodic Memory in Humans and Animals: An Integrative Framework We review the psychology and neuroscience of reinforcement learning (RL), which has experienced significant progress in the past two decades, enabled by the comprehensive experimental study of simple learning and decision-making tasks. The field also has yet to see a prevalent consistent and rigorous approach for evaluating agent performance on holdout data. We … Adv. To … We developed a neural network that is trained to find rewards in a foraging task where reward locations are continuously changing. Here we demonstrate a previously unappreciated benefit of memory transformation, namely, its ability to enhance reinforcement learning in a dynamic environment. Reinforcement Learning and Episodic Memory in Humans and Animals: An Integrative Framework @article{Gershman2017ReinforcementLA, title={Reinforcement Learning and Episodic Memory in Humans and Animals: An Integrative Framework}, author={S. Gershman and N. Daw}, journal={Annual Review of Psychology}, year={2017}, volume={68}, … Sample-Efficient Deep Reinforcement Learning via Episodic Backward Update Su Young Lee, Sungik Choi, Sae-Young Chung School of Electrical Engineering, KAIST, Republic of Korea {suyoung.l, si_choi, schung}@kaist.ac.kr Abstract We propose Episodic Backward Update (EBU) – a novel deep reinforcement learn-ing algorithm with a direct value propagation. inspired by this biological episodic memory, and models one of the several different control systems used for behavioural decisions as suggested by neuroscience research [9]. Research on such episodic learning has revealed its unmistakeable traces in human behavior, developed theory to articulate algorithms Reinforcement Learning and Episodic Memory in Humans and Animals: An Integrative Framework Annu Rev Psychol. 1 branch 0 tags. 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