mlsanityData to 14 Oct 2026

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

arXiv 2412.14367 ↗First posted 18 Dec 2024cs.RO cs.LGcited by 0

On the map: Deep reinforcement learning methods → RL for robot navigation and UAV control

Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in RL for robot navigation and UAV control, one of 15 directions in Deep reinforcement learning methods. Builds on 5 of its 7 references that are in the map. First posted on arXiv 18 Dec 2024.

Builds on

5 of 7 references.

References · 5

  1. 2017Proximal Policy Optimization Algorithmscited by 15,215
  2. 2015Continuous control with deep reinforcement learningcited by 3,407
  3. 2015Trust Region Policy Optimizationcited by 2,580
  4. 2018Addressing Function Approximation Error in Actor-Critic Methodscited by 2,417
  5. 2016OpenAI Gymcited by 1,736

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  1. 2025Learning Agile Gate Traversal via Analytical Optimal Policy Gradient
  2. 2024Deep Reinforcement Learning-based Quadcopter Controller: A Practical Approach and Experiments
  3. 2025RAPTOR: A Foundation Policy for Quadrotor Control
  4. 2025Multitask Reinforcement Learning for Quadcopter Attitude Stabilization and Tracking using Graph Policy
  5. 2019Flying through a narrow gap using neural network: an end-to-end planning and control approach
  6. 2026Physics-Aware End-to-End Deep Reinforcement Learning for Quadcopter Control with Actuator Dynamics
  7. 2026Learning Agile Intruder Interception using Differentiable Quadrotor Dynamics
  8. 2020Deep Reinforcement Learning based Local Planner for UAV Obstacle Avoidance using Demonstration Data