  It is not until we become senior citizens do we recognise how much we took
maintaining a simple standing posture for granted. It is truly fascinating to
observe the magnitude of control the human brain exercises, in real time, to
activate and deactivate the lower body muscles and solve a multi-link 3D
inverted pendulum problem in order to maintain a stable standing posture. This
realisation is even more apparent when training an artificial intelligence (AI)
agent to maintain a standing posture of a digital musculoskeletal avatar due to
the error propagation problem. In this work we address the error propagation
problem by introducing an iterative training procedure for deep reinforcement
learning which allows the agent to learn a finite set of actions and how to
coordinate between them in order to achieve a stable standing posture. The
proposed training approach allowed the agent to increase standing duration from
4 seconds using the traditional training method to 348 seconds using the
proposed method. The proposed training method allowed the agent to generalise
and accommodate perception and actuation noise for almost 108 seconds.
