  Neural control is an exciting mystery which we instinctively master. Yet,
researchers have a hard time explaining the motor control trajectories.
Physiologically accurate biomechanical simulations can, to some extent, mimic
live subjects and help us form evidence-based hypotheses. In these simulated
environments, muscle excitations are typically calculated through inverse
dynamic optimizations which do not possess a closed-form solution. Thus,
computationally expensive, and occasionally unstable, iterative numerical
solvers are the only widely utilized solution. In this work, we introduce
ArtiSynth, a 3D modeling platform that supports the combined simulation of
multi-body and finite element models, and extended to support reinforcement
learning (RL) training. we further use ArtiSynth to investigate whether a deep
RL policy can be trained to drive the motor control of a physiologically
accurate biomechanical model in a large continuous action space. We run a
comprehensive evaluation of its performance and compare the results with the
forward dynamics assisted tracking with a quadratic objective function. We
assess the two approaches in terms of correctness, stability,
energy-efficiency, and temporal consistency.
