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.
@article{arxiv.2412.14367,
title = {Implementing TD3 to train a Neural Network to fly a Quadcopter through an FPV Gate},
author = {Patrick Thomas and Kevin Schroeder and Jonathan Black},
journal= {arXiv preprint arXiv:2412.14367},
year = {2024}
}