Noise Parameterization of Continuous Deep Reinforcement Learning for a Class of Non-linear System
Reinforcement learning (RL) is one of the most important algorithms for artificial intelligence. DDPG as continuous controller approach which can work at continuous and high dimensional data applies in this paper to solve nonlinear valve system. The aim of this paper is gaining analysis of the DDPG...
Main Authors: | Surriani, Atikah, Wahyunggoro, Oyas, Cahyadi, Adha Imam |
---|---|
Format: | Conference or Workshop Item |
Language: | English |
Published: |
2022
|
Subjects: | |
Online Access: | https://repository.ugm.ac.id/282134/1/Surriani%20et%20al%20-%202022%20-%20Noise_Parameterization_of_Continuous_Deep_Reinforcement_Learning_for_a_Class_of_Non-linear_System.pdf |
Similar Items
-
Vertical Take-Off and Landing (VTOL) System Based On Noise Parameter for Exploring of Continuous Reinforcement Learning Approach
by: Surriani, Atikah, et al.
Published: (2022) -
Simulation Based Motion Planning of WMR Under Local Minimum Condition Using IAPF Algorithm
by: Puriyanto, Riky Dwi, et al.
Published: (2022) -
Quadrotor Inertial Navigation Aided by Vehicle Dynamic Model: A Nonlinear Observability Analysis
by: Putra, Bagaskara Primastya, et al.
Published: (2022) -
Tree Boosting Methods Comparison for Landslide Susceptibility Maps, Case Study: Kejajar, Wonosobo
by: Arifianto, Rokhmat, et al.
Published: (2023) -
Noise in Indonesian Urban Areas: Rules and Facts
by: Mediastika, Christina E., et al.
Published: (2022)