Policy Return: A New Method for Reducing the Number of Experimental Trials in Deep Reinforcement Learning
Using the same algorithm and hyperparameter configurations, deep reinforcement learning (DRL) will derive drastically different results from multiple experimental trials, and most of these results are unsatisfactory. Because of the instability of the results, researchers have to perform many trials...
I tiakina i:
| Ngā kaituhi matua: | , , |
|---|---|
| Hōputu: | Artigo |
| Reo: | Inglês |
| I whakaputaina: |
IEEE
2020-01-01
|
| Rangatū: | IEEE Access |
| Ngā marau: | |
| Urunga tuihono: | https://ieeexplore.ieee.org/document/9298771/ |
| Ngā Tūtohu: |
Kāore He Tūtohu, Me noho koe te mea tuatahi ki te tūtohu i tēnei pūkete!
|