| Title |
Efficient Reinforcement Learning Method for Dynamic System Control |
| Authors |
박채훈(Chaehun Park) ; 정철민(Cheolmin Jeong) ; 유재현(Jaehyun Yoo) ; 강창묵(Chang Mook Kang) |
| DOI |
https://doi.org/10.5370/KIEE.2022.71.9.1293 |
| Keywords |
Model based learning; PILCO; Gaussian process; External Control; Rotary inverted pendulum |
| Abstract |
Reinforcement learning is a method in which the controller interacts with the target system to collect data and utilizes it to evaluate and update itself. The common disadvantages of reinforcement learning are that it takes a considerable amount of time from initial random controls to valid controls, and that it can fall into local optima. For this reason, a method for improving learning efficiency by applying verified external control is being studied. In this paper, external control is utilized for data collection, making it easier to access data that is helpful for learning. This method not only improved learning efficiency, but also was able to derive a more stable controller than the external control by learning. To prove this, we applied the proposed method to RIP(Rotary Inverted Pendulum), which is used for controller stability experiments, and as external controls, swing-up and balance controls, which are commonly used for RIP control, were utilized. |