Adaptive Gain Scheduling using Reinforcement Learning for Quadcopter
  Control
Mike Timmerman Aryan Patel Tim Reinhart
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.