To swing up an inverted Pendulum using stochastic real-valued Reinforcement Learning

ICANN ’94 ◽  
1994 ◽  
pp. 655-658
Author(s):  
A. Standfuss ◽  
R. Eckmiller
Keyword(s):  
Reinforcement Learning ◽  
Inverted Pendulum

Control of an Inverted Pendulum by Reinforcement Learning Method in PLC Environment

2020 ◽  
Author(s):  
Gokhan Demirkiran ◽  
Ozcan Erdener ◽  
Onay Akpinar ◽  
Pelin Demirtas ◽  
M. Yagiz Arik ◽  
...  
Keyword(s):  
Reinforcement Learning ◽  
Inverted Pendulum ◽  
Learning Method

scholarly journals A real-time HIL control system on rotary inverted pendulum hardware platform based on double deep Q-network

2021 ◽  
Vol 54 (3-4) ◽  
pp. 417-428
Author(s):  
Yanyan Dai ◽  
KiDong Lee ◽  
SukGyu Lee
Keyword(s):  
Control System ◽  
Reinforcement Learning ◽  
Inverted Pendulum ◽  
Learning Algorithm ◽  
Deep Understanding ◽  
Control Engineering ◽  
Experience Replay ◽  
Real Hardware ◽  
Rotary Inverted Pendulum ◽  
Reinforcement Learning Algorithm

For real applications, rotary inverted pendulum systems have been known as the basic model in nonlinear control systems. If researchers have no deep understanding of control, it is difficult to control a rotary inverted pendulum platform using classic control engineering models, as shown in section 2.1. Therefore, without classic control theory, this paper controls the platform by training and testing reinforcement learning algorithm. Many recent achievements in reinforcement learning (RL) have become possible, but there is a lack of research to quickly test high-frequency RL algorithms using real hardware environment. In this paper, we propose a real-time Hardware-in-the-loop (HIL) control system to train and test the deep reinforcement learning algorithm from simulation to real hardware implementation. The Double Deep Q-Network (DDQN) with prioritized experience replay reinforcement learning algorithm, without a deep understanding of classical control engineering, is used to implement the agent. For the real experiment, to swing up the rotary inverted pendulum and make the pendulum smoothly move, we define 21 actions to swing up and balance the pendulum. Comparing Deep Q-Network (DQN), the DDQN with prioritized experience replay algorithm removes the overestimate of Q value and decreases the training time. Finally, this paper shows the experiment results with comparisons of classic control theory and different reinforcement learning algorithms.

Reinforcement Learning Compensation based PD Control for a Double Inverted Pendulum

2019 ◽  
Vol 17 (02) ◽  
pp. 323-329 ◽  
Author(s):  
Guillermo Puriel Gil ◽  
Wen Yu ◽  
Humberto Sossa
Keyword(s):  
Reinforcement Learning ◽  
Inverted Pendulum ◽  
Pd Control

scholarly journals Imitation Reinforcement Learning-Based Remote Rotary Inverted Pendulum Control in OpenFlow Network

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 36682-36690 ◽  
Author(s):  
Ju-Bong Kim ◽  
Hyun-Kyo Lim ◽  
Chan-Myung Kim ◽  
Min-Suk Kim ◽  
Yong-Geun Hong ◽  
...  
Keyword(s):  
Reinforcement Learning ◽  
Inverted Pendulum ◽  
Rotary Inverted Pendulum

Reinforcement Learning for Cart Pole Inverted Pendulum System

2021 ◽  
Author(s):  
Atikah Surriani ◽  
Oyas Wahyunggoro ◽  
Adha Imam Cahyadi
Keyword(s):  
Reinforcement Learning ◽  
Inverted Pendulum ◽  
Pendulum System ◽  
Inverted Pendulum System
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