State-Temporal Compression in Reinforcement Learning With the Reward-Restricted Geodesic Metric
It is difficult to solve complex tasks that involve large state spaces and long-term decision processes by reinforcement learning (RL) algorithms. A common and promising method to address this challenge is to compress a large RL problem into a small one. Towards this goal, the compression should be...
Ausführliche Beschreibung
Bibliographische Detailangaben
| Veröffentlicht in: | IEEE transactions on pattern analysis and machine intelligence. - 1979. - 44(2022), 9 vom: 20. Sept., Seite 5572-5589
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| 1. Verfasser: |
Guo, Shangqi
(VerfasserIn) |
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| Weitere Verfasser: |
Yan, Qi,
Su, Xin,
Hu, Xiaolin,
Chen, Feng |
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| Format: | Online-Aufsatz
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| Sprache: | English |
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| Veröffentlicht: |
2022
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| Zugriff auf das übergeordnete Werk: | IEEE transactions on pattern analysis and machine intelligence
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| Schlagworte: | Journal Article |
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