用TD3训练神经网络让无人机穿越门洞，真实环境验证成功。
Implementing TD3 to train a Neural Network to fly a Quadcopter through an FPV Gate
- 用TD3算法训练无人机速度控制器，实现端到端飞行决策。
- 在真实实验室环境中成功让无人机穿越门洞且未撞毁。
- 适合对强化学习控制无人机感兴趣的开发者参考。
深度强化学习在最优解不明确的环境中展现出了强大的策略开发能力。本文尝试应用双延迟深度确定性策略梯度（TD3）来训练一个神经网络，使其作为四轴无人机的速度控制器。无人机的目标是在不撞上门框的前提下快速穿越门洞。通过将训练好的策略部署到实验室环境中的真实四轴无人机上，我们最终证明该策略能够在真实世界中引导无人机成功到达门洞。
原文摘要 · Abstract (English)
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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