Learning Achievement Structure for Structured Exploration in Domains with Sparse RewardDownload PDF

Anonymous

22 Sept 2022, 12:41 (modified: 16 Nov 2022, 05:40)ICLR 2023 Conference Blind SubmissionReaders: Everyone
Keywords: deep reinforcement learning, structured exploration
Abstract: We propose Structured Exploration with Achievements (SEA), a multi-stage reinforcement learning algorithm that learns the environment structure with offline data and uses the learned structure to learn different skills and improve overall exploration with online environment interactions in a particular type of environment that has an internal achievement system. SEA first uses a contrast-based loss function to learn the achievement representations and build an achievement classifier. It then tries to recover the environment achievement structure with a heuristic algorithm. Finally, SEA builds a meta-controller with the recovered structure to learn sub-policies and explore new tasks. While exploration in a procedurally generated environment with high-dimensional input like images is extremely hard for reinforcement learning agents, we demonstrate that SEA is still able to recover the underlying structure and explore new tasks in different domains.
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