The Double Deep Policy Gradient or DDPG Reinforcement Learning algorithm has a tendency to overestimate computations, which results in learning policies that are not optimum. When DDPG agent is applied in the rocket landing control system, high frequency chattering occurs at the main engine’s control command and this gives substantial drawbacks for the liquid propulsion life longevity. The Twin-Delayed Deep Deterministic Policy Gradient (TD3) Reinforcement Learning agent is proposed to overcame this chattering by delaying the landing rocket control command, as simulated in two dimensions. This TD3 controller is shown to be able to dampen the engine command as its achievements are contrasted with the outcomes of the DDPG one.
Improvement of the DDPG algorithm via twin delayed DDPG (TD3) on vertical rocket landing control system
THE 9TH INTERNATIONAL SEMINAR ON AEROSPACE SCIENCE AND TECHNOLOGY – ISAST 2022 ; 2022 ; Bogor, Indonesia
AIP Conference Proceedings ; 2941 , 1
2023-12-11
8 pages
Conference paper
Electronic Resource
English
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