  Motor control is a set of time-varying muscle excitations which generate
desired motions for a biomechanical system. Muscle excitations cannot be
directly measured from live subjects. An alternative approach is to estimate
muscle activations using inverse motion-driven simulation. In this article, we
propose a deep reinforcement learning method to estimate the muscle excitations
in simulated biomechanical systems. Here, we introduce a custom-made reward
function which incentivizes faster point-to-point tracking of target motion.
Moreover, we deploy two new techniques, namely, episode-based hard update and
dual buffer experience replay, to avoid feedback training loops. The proposed
method is tested in four simulated 2D and 3D environments with 6 to 24 axial
muscles. The results show that the models were able to learn muscle excitations
for given motions after nearly 100,000 simulated steps. Moreover, the root mean
square error in point-to-point reaching of the target across experiments was
less than 1% of the length of the domain of motion. Our reinforcement learning
method is far from the conventional dynamic approaches as the muscle control is
derived functionally by a set of distributed neurons. This can open paths for
neural activity interpretation of this phenomenon.
