Factored State Abstraction for Option Learning
Hierarchical reinforcement learning has focused on discovering temporally extended actions (options) to provide efficient solutions for long-horizon decision-making problems with sparse rewards. One promising approach that learns these options end-toend in this setting is the option-critic (OC) fram...
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| Format: | Thesis |
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Massachusetts Institute of Technology
2022
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| Online Access: | https://hdl.handle.net/1721.1/140090 |