The paper presents a technique using reinforcement learning (RL) to adapt the
control gains of a quadcopter controller. Specifically, we employed Proximal
Policy Optimization (PPO) to train a policy which adapts the gains of a
cascaded feedback controller in-flight. The primary goal of this controller is
to minimize tracking error while following a specified trajectory. The paper's
key objective is to analyze the effectiveness of the adaptive gain policy and
compare it to the performance of a static gain control algorithm, where the
Integral Squared Error and Integral Time Squared Error are used as metrics. The
results show that the adaptive gain scheme achieves over 40$\%$ decrease in
tracking error as compared to the static gain controller.

本文通过使用强化学习技术 (Reinforcement Learning, RL) 来调整四旋翼控制器的控制增益，特别地，我们采用了近端策略优化 (Proximal Policy Optimization, PPO) 来训练一个根据实际情况调整控制增益的策略，其中主要目标是在跟随预定轨迹的同时最小化跟踪误差。本文的主要目的是分析自适应增益策略的有效性，并将其与静态增益控制算法的性能进行比较，使用积分平方误差和积分时间平方误差作为度量标准。结果表明，与静态增益控制器相比，自适应增益方案实现了超过 40% 的跟踪误差降低。