Actor-Critic強化学習によるPID制御の自動チューニング
Autotuning PID control using Actor-Critic Deep Reinforcement Learning
リンゴ収穫ロボットのPIDパラメータをA2C強化学習で最適化し、ベースラインを上回る性能とリンゴ位置に応じた適応制御を実現した。
分類: cs.LG, cs.AI, cs.RO
原文アブストラクト
This work is an exploratory research concerned with determining in what way reinforcement learning can be used to predict optimal PID parameters for a robot designed for apple harvest. To study this, an algorithm called Advantage Actor Critic (A2C) is implemented on a simulated robot arm. The simulation primarily relies on the ROS framework. Experiments for tuning one actuator at a time and two actuators a a time are run, which both show that the model is able to predict PID gains that perform better than the set baseline. In addition, it is studied if the model is able to predict PID parameters based on where an apple is located. Initial tests show that the model is indeed able to adapt its predictions to apple locations, making it an adaptive controller.