Does tabular Q-learning outperform Deep learning based method in small scenario?
Best insight from top research papers
Tabular $Q$-learning, as proposed in Context_1, is utilized to learn the shortest path in a fire evacuation environment. It pretrains a DQN-based agent with information on the shortest path, achieving optimal evacuation paths. On the other hand, Deep Learning methods, like the FL-SegNet model in Context_3, are designed for pixel-level defect detection in tunnel linings. While tabular $Q$-learning excels in learning optimal paths in structured environments, Deep Learning methods are tailored for complex image analysis tasks. In small scenarios, the performance of tabular $Q$-learning may outperform Deep Learning methods due to its focus on learning optimal paths efficiently . However, the choice between the two approaches ultimately depends on the specific task requirements and the nature of the problem at hand.
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| Papers (5) | Insight |
|---|---|
11 Citations | Not addressed in the paper. |
1 Citations | Not addressed in the paper. |
40 Citations | Tabular Q-learning is not compared to deep learning in the paper. The FL-SegNet method excels in accurately detecting small-sized damages in tunnel linings. |
| Tabular Q-learning outperforms Deep Q-Learning in small scenarios by pretraining network weights with shortest path information, enhancing evacuation planning in fire emergencies. | |
145 Citations | Not addressed in the paper. |
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