  Learning a locomotion controller for a musculoskeletal system is challenging
due to over-actuation and high-dimensional action space. While many
reinforcement learning methods attempt to address this issue, they often
struggle to learn human-like gaits because of the complexity involved in
engineering an effective reward function. In this paper, we demonstrate that
adversarial imitation learning can address this issue by analyzing key problems
and providing solutions using both current literature and novel techniques. We
validate our methodology by learning walking and running gaits on a simulated
humanoid model with 16 degrees of freedom and 92 Muscle-Tendon Units, achieving
natural-looking gaits with only a few demonstrations.
