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• W3005727454 abstract "Can algorithms with a representation solve reinforcement learning problems? In this work, we answer this question in the affirmative, where we take simple learning to be tabular Q-Learning, the good representations to be a learned state abstraction, and challenging to be continuous control tasks. Our main contribution is a learning algorithm that abstracts a continuous state-space into a discrete one. We transfer this learned representation to unseen problems to enable effective learning. We provide theory showing that learned abstractions maintain a bounded value loss, and we report experiments showing that the abstractions empower tabular Q-Learning to learn efficiently in unseen tasks." @default.
• W3005727454 created "2020-02-24" @default.
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• W3005727454 date "2020-02-08" @default.
• W3005727454 modified "2023-09-23" @default.
• W3005727454 title "Learning State Abstractions for Transfer in Continuous Control." @default.
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• W3005727454 hasPublicationYear "2020" @default.
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