Name
Title | ||
|---|---|---|
An approach to tune fuzzy controllers based on reinforcement learning for autonomous vehicle control |
Abstract | ||
|---|---|---|
In this paper, we suggest a new approach for tuning parameters of fuzzy controllers based on reinforcement learning. The architecture of the proposed approach is comprised of a Q estimator network (QEN) and a Takagi-Sugeno-type fuzzy inference system (TSK-FIS). Unlike other fuzzy Q-learning approaches that select an optimal action based on finite discrete actions, the proposed controller obtains the control output directly from TSK-FIS. With the proposed architecture, the learning algorithms for all the parameters of the QEN and the FIS are developed based on the temporal-difference (TD) methods as well as the gradient-descent algorithm. The performance of the proposed design technique is illustrated by simulation studies of a vehicle longitudinal-control system. |
Year | DOI | Venue |
|---|---|---|
2005 | 10.1109/TITS.2005.853698 | IEEE Transactions on Intelligent Transportation Systems |
Keywords | DocType | Volume |
fuzzy controller,takagi-sugeno-type fuzzy inference system,vehicle longitudinal-control system,proposed controller,fuzzy q-learning approach,reinforcement learning,new approach,proposed design technique,autonomous vehicle control,proposed architecture,temporal difference,control system,learning artificial intelligence,gradient descent,fuzzy control,fuzzy systems | Journal | 6 |
Issue | ISSN | Citations |
3 | 1524-9050 | 17 |
PageRank | References | Authors |
0.97 | 20 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xiaohui Dai | 1 | 44 | 2.74 |
| Chi-Kwong Li | 2 | 313 | 29.81 |
| Ahmad B. Rad | 3 | 273 | 30.64 |