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EMDD-RL: faster subgoal identification with diverse density in reinforcement learning
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12626042.pdf
Date
2021-1-15
Author
Sunel, Saim
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Diverse Density (DD) algorithm is a well-known multiple instance learning method, also known to be effective to automatically identify sub-goals and improve Reinforcement Learning (RL). Expectation-Maximization Diverse Density (EMDD) improves DD in terms of both speed and accuracy. This study adapts EMDD to automatically identify subgoals for RL which is shown to perform significantly faster (3 to 10 times) than its predecessor, without sacrificing solution quality. The performance of the proposed method named EMDD-RL is empirically shown via extensive experimentation, together with the discussions on the effects of EMDD hyperparameters on the results.
Subject Keywords
Subgoal identification
,
Expectation-Maximization Diverse Density
,
Diverse Density
,
Reinforcement Learning
URI
https://hdl.handle.net/11511/89597
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Graduate School of Natural and Applied Sciences, Thesis
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S. Sunel, “EMDD-RL: faster subgoal identification with diverse density in reinforcement learning,” M.S. - Master of Science, Middle East Technical University, 2021.