  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.
