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arXiv · 2412.14367

Implementing TD3 to train a Neural Network to fly a Quadcopter through an FPV Gate

Patrick Thomas·Kevin Schroeder·Jonathan Black
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Abstract

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.

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Patrick Thomas, Kevin Schroeder, Jonathan Black. 2024-12-18. Implementing TD3 to train a Neural Network to fly a Quadcopter through an FPV Gate. https://arxiv.org/abs/2412.14367

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