Sequential Association Rule Mining for Autonomously Extracting Hierarchical Task Structures in Reinforcement Learning
Reinforcement learning (RL) techniques, while often powerful, can suffer from slow learning speeds, particularly in high dimensional spaces or in environments with sparse rewards. The decomposition of tasks into a hierarchical structure holds the potential to significantly speed up learning, general...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2020-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/8957114/ |