Surriani, Atikah and Wahyunggoro, Oyas and Cahyadi, Adha Imam (2021) Reinforcement Learning for Cart Pole Inverted Pendulum System. In: Industrial Electronics and Applications Conference, IEACon.
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Abstract
Recently, reinforcement learning considered to be the chosen method to solve many problems. One of the challenging problems is controlling dynamic behaviour systems. This paper used policy gradient to balance cart pole inverted pendulum. The purpose of this paper is to balance the pole upright with the movement of the cart. The paper employed two main policy gradient-based algorithms. The results show that PG using baseline has faster episodes than reinforce PG in the training process, reinforce PG algorithm got higher accumulative reward value than PG using baseline. © 2021 IEEE.
Item Type: | Conference or Workshop Item (Paper) |
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Additional Information: | Cited by: 11 |
Uncontrolled Keywords: | Pendulums; Reinforcement learning; Behavior systems; Cart pole inverted pendulum; Dynamic behaviors; Gradient based algorithm; Inverted pendulum; Inverted pendulum system; Policy gradient; Policy gradient baseline; Reinforcement learnings; Poles |
Subjects: | T Technology > TK Electrical engineering. Electronics Nuclear engineering |
Divisions: | Faculty of Engineering > Electronics Engineering Department |
Depositing User: | Sri JUNANDI |
Date Deposited: | 25 Oct 2024 03:35 |
Last Modified: | 25 Oct 2024 03:35 |
URI: | https://ir.lib.ugm.ac.id/id/eprint/8640 |