将来の参照情報を活用したPPOによる追従制御
Proximal Policy Optimization for Tracking Control Exploiting Future Reference Information
将来の参照値を俳優と評論家に入力する2つの手法を提案し、PPOの追従性能を向上させてPI制御と比較評価した論文。
著者: Jana Mayer, Johannes Westermann, Juan Pedro Gutiérrez H. Muriedas, Uwe Mettin, Alexander Lampe
分類: cs.LG, cs.RO, cs.SY, eess.SY
原文アブストラクト
In recent years, reinforcement learning (RL) has gained increasing attention in control engineering. Especially, policy gradient methods are widely used. In this work, we improve the tracking performance of proximal policy optimization (PPO) for arbitrary reference signals by incorporating information about future reference values. Two variants of extending the argument of the actor and the critic taking future reference values into account are presented. In the first variant, global future reference values are added to the argument. For the second variant, a novel kind of residual space with future reference values applicable to model-free reinforcement learning is introduced. Our approach is evaluated against a PI controller on a simple drive train model. We expect our method to generalize to arbitrary references better than previous approaches, pointing towards the applicability of RL to control real systems.