Name
Abstract | ||
|---|---|---|
Model-based reinforcement learning (MBRL) has been proposed as a promising alternative solution to tackle the high sampling cost challenge in the canonical RL, by leveraging a system dynamics model to generate synthetic data for policy training purpose. The MBRL framework, nevertheless, is inherently limited by the convoluted process of jointly optimizing control policy, learning system dynamics, ... |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1109/TNNLS.2020.3008249 | IEEE Transactions on Neural Networks and Learning Systems |
Keywords | DocType | Volume |
Training,Robots,Task analysis,Process control,Data models,Training data,Learning systems | Journal | 32 |
Issue | ISSN | Citations |
6 | 2162-237X | 0 |
PageRank | References | Authors |
0.34 | 14 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Linsen Dong | 1 | 3 | 1.78 |
| Yuanlong Li | 2 | 10 | 3.24 |
| Xin Zhou | 3 | 126 | 15.50 |
| Yonggang Wen | 4 | 2512 | 156.47 |
| Kyle Guan | 5 | 338 | 20.73 |