  Reinforcement learning (RL) is a popular data-driven method that has
demonstrated great success in robotics. Previous works usually focus on
learning an end-to-end (direct) policy to directly output joint torques. While
the direct policy seems convenient, the resultant performance may not meet our
expectations. To improve its performance, more sophisticated reward functions
or more structured policies can be utilized. This paper focuses on the latter
because the structured policy is more intuitive and can inherit insights from
previous model-based controllers. It is unsurprising that the structure, such
as a better choice of the action space and constraints of motion trajectory,
may benefit the training process and the final performance of the policy at the
cost of generality, but the quantitative effect is still unclear. To analyze
the effect of the structure quantitatively, this paper investigates three
policies with different levels of structure in learning quadruped locomotion: a
direct policy, a structured policy, and a highly structured policy. The
structured policy is trained to learn a task-space impedance controller and the
highly structured policy learns a controller tailored for trot running, which
we adopt from previous work. To evaluate trained policies, we design a
simulation experiment to track different desired velocities under force
disturbances. Simulation results show that structured policy and highly
structured policy require 1/3 and 3/4 fewer training steps than the direct
policy to achieve a similar level of cumulative reward, and seem more robust
and efficient than the direct policy. We highlight that the structure embedded
in the policies significantly affects the overall performance of learning a
complicated task when complex dynamics are involved, such as legged locomotion.
