  Legged systems have many advantages when compared to their wheeled
counterparts. For example, they can more easily navigate extreme, uneven
terrain. However, there are disadvantages as well, particularly the difficulty
seen in modeling the nonlinearities of the system. Research has shown that
using flexible components within legged locomotive systems improves performance
measures such as efficiency and running velocity. Because of the difficulties
encountered in modeling flexible systems, control methods such as reinforcement
learning can be used to define control strategies. Furthermore, reinforcement
learning can be tasked with learning mechanical parameters of a system to match
a control input. It is shown in this work that when deploying reinforcement
learning to find design parameters for a pogo-stick jumping system, the designs
the agents learn are optimal within the design space provided to the agents.
