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Option discovery in reinforcement learning using frequent common subsequences of actions
Date
2005-11-30
Author
Girgin, Sertan
Polat, Faruk
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This work is licensed under a
Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License
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Temporally abstract actions, or options, facilitate learning in large and complex domains by exploiting sub-tasks and hierarchical structure of the problem formed by these sub-tasks. In this paper, we study automatic generation of options using common sub-sequences derived from the state transition histories collected as learning progresses. The standard Q-learning algorithm is extended to use generated options transparently, and effectiveness of the method is demostrated in Dietterich's Taxi domain.
URI
https://hdl.handle.net/11511/55280
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Department of Computer Engineering, Conference / Seminar