Implementing TD3 to train a Neural Network to fly a Quadcopter through an FPV Gate
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arXiv
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| Autores principales: | , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| Acceso en línea: | |
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| _version_ | 1866929639818002432 |
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| author | Thomas, Patrick Schroeder, Kevin Black, Jonathan |
| author_facet | Thomas, Patrick Schroeder, Kevin Black, Jonathan |
| contents | Deep Reinforcement learning has shown to be a powerful tool for developing policies in environments where an optimal solution is unclear. In this paper, we attempt to apply Twin Delayed Deep Deterministic Policy Gradients to train a neural network to act as a velocity controller for a quadcopter. The quadcopter's objective is to quickly fly through a gate while avoiding crashing into the gate. We transfer our trained policy to the real world by deploying it on a quadcopter in a laboratory environment. Finally, we demonstrate that the trained policy is able to navigate the drone to the gate in the real world. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_14367 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Implementing TD3 to train a Neural Network to fly a Quadcopter through an FPV Gate Thomas, Patrick Schroeder, Kevin Black, Jonathan Robotics Machine Learning Deep Reinforcement learning has shown to be a powerful tool for developing policies in environments where an optimal solution is unclear. In this paper, we attempt to apply Twin Delayed Deep Deterministic Policy Gradients to train a neural network to act as a velocity controller for a quadcopter. The quadcopter's objective is to quickly fly through a gate while avoiding crashing into the gate. We transfer our trained policy to the real world by deploying it on a quadcopter in a laboratory environment. Finally, we demonstrate that the trained policy is able to navigate the drone to the gate in the real world. |
| title | Implementing TD3 to train a Neural Network to fly a Quadcopter through an FPV Gate |
| topic | Robotics Machine Learning |
| url | https://arxiv.org/abs/2412.14367 |