Guided Reinforcement Learning with Twin Delayed Deep Deterministic Policy Gradient for a Rotary Flexible-Link System

Guided Reinforcement Learning with Twin Delayed Deep Deterministic Policy Gradient for a Rotary Flexible-Link System

This study proposes a robust methodology for vibration suppression and trajectory tracking in rotary flexible-link systems by leveraging guided reinforcement learning (GRL). The approach integrates the twin delayed deep deterministic policy gradient (TD3) algorithm with a linear quadratic regulator...

Full description

Saved in:
Bibliographic Details
Main Authors: Carlos Saldaña Enderica, José Ramon Llata, Carlos Torre-Ferrero
Format: Article
Language:English
Published: MDPI AG 2025-05-01
Series:Robotics
Subjects:
guided reinforcement learning
deep reinforcement learning
TD3
linear quadratic regulator
hybrid control
vibration suppression
Online Access:https://www.mdpi.com/2218-6581/14/6/76
Tags: Add Tag
No Tags, Be the first to tag this record!

Internet

https://www.mdpi.com/2218-6581/14/6/76

Similar Items