データからのイベントトリガ制御の階層強化学習による同時最適化学習
Learning Event-triggered Control from Data through Joint Optimization
通信タイミングと制御内容を階層強化学習で同時に最適化し、6自由度マニピュレータ実機で有効性を示したモデルフリーなイベントトリガ制御学習フレームワーク。
著者: Niklas Funk, Dominik Baumann, Vincent Berenz, Sebastian Trimpe
分類: eess.SY, cs.LG, cs.RO, cs.SY
原文アブストラクト
We present a framework for model-free learning of event-triggered control strategies. Event-triggered methods aim to achieve high control performance while only closing the feedback loop when needed. This enables resource savings, e.g., network bandwidth if control commands are sent via communication networks, as in networked control systems. Event-triggered controllers consist of a communication policy, determining when to communicate, and a control policy, deciding what to communicate. It is essential to jointly optimize the two policies since individual optimization does not necessarily yield the overall optimal solution. To address this need for joint optimization, we propose a novel algorithm based on hierarchical reinforcement learning. The resulting algorithm is shown to accomplish high-performance control in line with resource savings and scales seamlessly to nonlinear and high-dimensional systems. The method's applicability to real-world scenarios is demonstrated through experiments on a six degrees of freedom real-time controlled manipulator. Further, we propose an approach towards evaluating the stability of the learned neural network policies.
