  We present computational and experimental results on how artificial
intelligence (AI) learns to control an Acrobot using reinforcement learning
(RL). Thereby the experimental setup is designed as an embedded system, which
is of interest for robotics and energy harvesting applications. Specifically,
we study the control of angular velocity of the Acrobot, as well as control of
its total energy, which is the sum of the kinetic and the potential energy. By
this means the RL algorithm is designed to drive the angular velocity or the
energy of the first pendulum of the Acrobot towards a desired value. With this,
libration or full rotation of the unactuated pendulum of the Acrobot is
achieved. Moreover, investigations of the Acrobot control are carried out,
which lead to insights about the influence of the state space discretization,
the episode length, the action space or the mass of the driven pendulum on the
RL control. By further numerous simulations and experiments the effects of
parameter variations are evaluated.
