  We propose a method of learning a policy for human-like locomotion via deep
reinforcement learning based on a human anatomical model, muscle actuation, and
biologically inspired rewards, without any inherent control rules or reference
motions. Our main ideas involve providing a dense reward using metabolic energy
consumption at every step during the initial stages of learning and then
transitioning to a sparse reward as learning progresses, and adjusting the
initial posture of the human model to facilitate the exploration of locomotion.
Additionally, we compared and analyzed differences in learning outcomes across
various settings other than the proposed method.
