  Reinforcement learning is an emerging approach to control dynamical systems
for which classical approaches are difficult to apply. However, trained agents
may not generalize against the variations of system parameters. This paper
presents the concept of dual-scale homogeneity, an important property in
understating the scaling behavior of nonlinear systems. Furthermore, it also
presents an effective yet simple approach to designing a parameter-dependent
control law that homogenizes a nonlinear system. The presented approach is
applied to two systems, demonstrating its ability to provide a consistent
performance irrespective of parameters variations. To demonstrate the
practicality of the proposed approach, the control policy is generated by a
deep deterministic policy gradient to control the load position of a quadrotor
with a slung load. The proposed synergy between the homogeneity transformations
and reinforcement learning yields superior performance compared to other recent
learning-based control techniques. It achieves a success rate of 96% in
bringing the load to its designated target with a 3D RMSE of 0.0253 m. The
video that shows the experimental results along with a summary of the paper is
available at this link.
