Q学習によるロボット倒立振子の連続制御
A Q-learning approach to the continuous control problem of robot inverted pendulum balancing
実機データから同定したシミュレータ上でQ学習を行い、離散行動空間アルゴリズムで連続制御問題である倒立振子のバランス制御を実ロボットに適用した研究。
著者: Mohammad Safeea, Pedro Neto
分類: cs.RO, cs.LG
原文アブストラクト
This study evaluates the application of a discrete action space reinforcement learning method (Q-learning) to the continuous control problem of robot inverted pendulum balancing. To speed up the learning process and to overcome technical difficulties related to the direct learning on the real robotic system, the learning phase is performed in simulation environment. A mathematical model of the system dynamics is implemented, deduced by curve fitting on data acquired from the real system. The proposed approach demonstrated feasible, featuring its application on a real world robot that learned to balance an inverted pendulum. This study also reinforces and demonstrates the importance of an accurate representation of the physical world in simulation to achieve a more efficient implementation of reinforcement learning algorithms in real world, even when using a discrete action space algorithm to control a continuous action.
