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arXiv:2306.15934cs.LG

Curious Replay for Model-based Adaptation

Isaac Kauvar, Chris Doyle, Linqi Zhou, Nick Haber

Abstract

Agents must be able to adapt quickly as an environment changes. We find that existing model-based reinforcement learning agents are unable to do this well, in part because of how they use past experiences to train their world model. Here, we present Curious Replay -- a form of prioritized experience replay tailored to model-based agents through use of a curiosity-based priority signal. Agents using Curious Replay exhibit improved performance in an exploration paradigm inspired by animal behavior and on the Crafter benchmark. DreamerV3 with Curious Replay surpasses state-of-the-art performance on Crafter, achieving a mean score of 19.4 that substantially improves on the previous high score of 14.5 by DreamerV3 with uniform replay, while also maintaining similar performance on the Deepmind Control Suite. Code for Curious Replay is available at https://github.com/AutonomousAgentsLab/curiousreplay

arXiv comments
Accepted at ICML 2023. Website at https://sites.google.com/view/curious-replay
Journal reference
DOI
Subjects
cs.LGMachine Learning cs.AIArtificial Intelligence stat.MLMachine Learning (Statistics)

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    • Direct arxiv link: https://arxiv.org/pdf/2306.15934.pdf

      piyhView on HNParent context

    • OK here is the arxiv https://arxiv.org/abs/2306.15934 called "Curious Replay for Model-based Adaptation" and from the abstract it says "we present Curious Replay -- a form of prioritized experience replay tailored to model-based agents through use of a curiosity-based priority…

      ftxbroView on HNParent context

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