| Título |
Regulation of a Van der Pol oscillator using reinforcement learning |
| Tipo |
Congreso |
| Sub-tipo |
Memoria |
| Descripción |
9th International Congress on Telematics and Computing, WITCOM 2020 |
| Resumen |
In this work, we propose a reinforcement learning-based methodology for the regulation problem of a Van der Pol oscillator with an actuator subject to constraints. We use two neural networks, one who learns an approximation of the cost given a state, and one that learns the controller output. We employ a classic PID controller with compensation as base policy in a rollout scheme. This policy is further improved by a neural network trained on trajectories from random initial states. The results show that the resulting control policy reduces the cost for a minimal energy trajectory given an initial state. |
| Observaciones |
DOI 978-303062553-5
Communications in Computer and Information Science |
| Lugar |
Puerto Vallarta |
| País |
Mexico |
| No. de páginas |
281-296 |
| Vol. / Cap. |
v. 1280 |
| Inicio |
2020-11-02 |
| Fin |
2020-11-06 |
| ISBN/ISSN |
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